{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"classification_images_adverserial-FGMS-defense.ipynb","provenance":[{"file_id":"1_MjroN9ydjEvuB9j33wLmb3VrKUFaYIN","timestamp":1567120521232},{"file_id":"1XzhorsaQwoUvMJyohuI8wZwoYLsTbSsB","timestamp":1566165853447}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CcDFB3Bk55Ey","colab_type":"code","outputId":"6eec29e9-b9e8-4a99-bbdb-0c45b0aff30c","executionInfo":{"status":"ok","timestamp":1569205527585,"user_tz":240,"elapsed":1252,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)"],"execution_count":71,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['f', 'test']\n","`%matplotlib` prevents importing * from pylab and numpy\n","  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"WDmAvekAs6S-","colab_type":"code","outputId":"894d15d5-cef4-4467-df06-12f6b3f4256c","executionInfo":{"status":"ok","timestamp":1569205529068,"user_tz":240,"elapsed":541,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":72,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gLHCEEBp5-wH","colab_type":"code","colab":{}},"source":["train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gyySx9TyRoDo","colab_type":"code","outputId":"2dc39493-76d7-4861-f5ab-d58a8a67e516","executionInfo":{"status":"ok","timestamp":1569205530462,"user_tz":240,"elapsed":454,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["test.data.shape"],"execution_count":74,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10000, 28, 28])"]},"metadata":{"tags":[]},"execution_count":74}]},{"cell_type":"code","metadata":{"id":"l8P08HhX6GHT","colab_type":"code","colab":{}},"source":["class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n","        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 500)\n","        self.fc2 = nn.Linear(500, 10)\n","\n","    def forward(self, x):\n","        x = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x = F.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return F.softmax(x, dim=1)\n","    \n","model = Model()\n","\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3) "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GmAhU-zZn25t","colab_type":"text"},"source":[""]},{"cell_type":"code","metadata":{"id":"nTFXgqIsWtOk","colab_type":"code","outputId":"4e6033c0-c0a9-47de-aa8c-64f7b3fde399","executionInfo":{"status":"ok","timestamp":1569200815912,"user_tz":240,"elapsed":2595,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# computing the average mnist images\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(5)\n","f.set_figwidth(15)\n","f.subplots_adjust(hspace=0.36) #, wspace=0.0, right = 0.8)\n","\n","\n","mean_imgs = []\n","for i in range(10):\n","  plt.figure()\n","  m = torch.mean(train.data[train.targets == i].type(torch.FloatTensor), dim=0)\n","  mean_imgs.append(m)\n","  axarr[i//5,i%5].imshow(m) \n","  "],"execution_count":26,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA00AAAEyCAYAAAAm6DsRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPZ0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x360 with 10 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"yG6WktvrRjAd","colab_type":"code","outputId":"c3bc3747-4519-4777-bf42-151ad50187af","executionInfo":{"status":"ok","timestamp":1569205580160,"user_tz":240,"elapsed":41531,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["EPOCHS = 5\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":76,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/5 [60000/60000 (100%)]\tLoss: 1.478875\t Test Accuracy: 95.8800%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/5 [60000/60000 (100%)]\tLoss: 1.486051\t Test Accuracy: 97.7400%\n"," Train Epoch: 3/5 [60000/60000 (100%)]\tLoss: 1.461599\t Test Accuracy: 98.0000%\n"," Train Epoch: 4/5 [60000/60000 (100%)]\tLoss: 1.485154\t Test Accuracy: 98.3000%\n"," Train Epoch: 5/5 [60000/60000 (100%)]\tLoss: 1.513038\t Test Accuracy: 97.3000%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pQK3c-aDmUru","colab_type":"code","outputId":"2ac492d3-d183-4dc9-f131-c98da5b0f2ce","executionInfo":{"status":"ok","timestamp":1569205580161,"user_tz":240,"elapsed":5971,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# # Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":77,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(97.3000, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"46tg5x5XLWrt","colab_type":"code","outputId":"ecee30f1-c209-4dc5-cac5-9adcc0db12a8","executionInfo":{"status":"ok","timestamp":1569205585649,"user_tz":240,"elapsed":1209,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import sklearn\n","from sklearn import metrics\n","# sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","aa = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","plt.imshow(aa, cmap = 'gray')\n","# plt.title('8 -> 9')"],"execution_count":78,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fe7ad037d68>"]},"metadata":{"tags":[]},"execution_count":78},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFEbgQGH/Bi0abxCpodtiAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"lV1qU8-ltKNm","colab_type":"code","colab":{}},"source":["model_clean = model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-ndiH517s9L2","colab_type":"code","colab":{}},"source":["\n","\n","import torch.nn.functional as F\n","def resize2d(img, size):\n","    return (F.adaptive_avg_pool2d(Variable(img,volatile=True), size)).data\n","\n","  \n","from skimage import io, transform  \n","import sklearn\n","from sklearn import metrics\n","\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg  # for reading image\n","import matplotlib.cm as cm\n","\n","import os\n","\n","# https://stackoverflow.com/questions/25862026/turn-off-axes-in-subplots/25864515\n","\n","\n","# The attack part\n","# https://pytorch.org/tutorials/beginner/fgsm_tutorial.html\n","\n","\n","from __future__ import print_function\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torchvision import datasets, transforms\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","\n","epsilons = [0, .05, .1, .15, .2, .25, .3]\n","# pretrained_model = \"data/lenet_mnist_model.pth\"\n","use_cuda=True\n","\n","\n","\n","model.eval()\n","\n","\n","epsilons = [.25]\n","\n","# FGSM attack code\n","def fgsm_attack(image, epsilon, data_grad):\n","    # Collect the element-wise sign of the data gradient\n","    sign_data_grad = data_grad.sign()\n","    # Create the perturbed image by adjusting each pixel of the input image\n","    perturbed_image = image + epsilon*sign_data_grad\n","    # Adding clipping to maintain [0,1] range\n","    perturbed_image = torch.clamp(perturbed_image, 0, 1)\n","    # Return the perturbed image\n","    return perturbed_image\n","  "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"p6yFwQKYukeg","colab_type":"code","colab":{}},"source":["dd, tt = next(iter(test_loader))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"C-ezqYbwim71","colab_type":"code","outputId":"2b0abab7-4bf9-4474-b38c-5e8119bdd961","executionInfo":{"status":"ok","timestamp":1567185607593,"user_tz":240,"elapsed":918,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(tt)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["256"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"id":"dgdY4V80tJxZ","colab_type":"code","colab":{}},"source":["def test( model, device, test_loader, epsilon ):\n","    \n","    data_grads = {}\n","    data_grads_GSM = {}\n","#     data_grads_after = {}    \n","    for i in range(10):\n","      data_grads[i] = []\n","      data_grads_GSM[i]= []      \n","#       data_grads_after[i] = []\n","    \n","    # Accuracy counter\n","    correct = 0\n","    adv_examples = []\n","\n","    # Loop over all examples in test set\n","    for data, target in test_loader:\n","#         import pdb; pdb.set_trace()\n","        \n","        # Send the data and label to the device\n","        data, target = data.to(device), target.to(device)\n","        \n","        # Set requires_grad attribute of tensor. Important for Attack\n","        data.requires_grad = True\n","\n","        # Forward pass the data through the model\n","        output = model(data)\n","        init_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n","\n","        # If the initial prediction is wrong, dont bother attacking, just move on\n","        if init_pred.item() != target.item():\n","            continue\n","\n","        # Calculate the loss\n","        loss = F.nll_loss(output, target)\n","\n","        # Zero all existing gradients\n","        model.zero_grad()\n","\n","        # Calculate gradients of model in backward pass\n","        loss.backward()\n","\n","        # Collect datagrad\n","        data_grad = data.grad.data\n","#         import pdb; pdb.set_trace()\n","#         data_grads[init_pred].append(data_grad)  ## save for later\n","        data_grads[target.cpu().numpy()[0]].append(data_grad)      # does not matter since both prediciton and target are the same here; i.e., prediction is correct  \n","        # data_grads[init_pred.cpu().numpy()[0][0]].append(data_grad)        \n","\n","        # Call FGSM Attack\n","        perturbed_data = fgsm_attack(data, epsilon, data_grad)\n","\n","        # Re-classify the perturbed image\n","        output = model(perturbed_data)\n","\n","        # Check for success\n","        final_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n","        if final_pred.item() == target.item():\n","            correct += 1\n","            # Special case for saving 0 epsilon examples\n","            if (epsilon == 0) and (len(adv_examples) < 5):\n","                adv_ex = perturbed_data.squeeze().detach().cpu().numpy()\n","                adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )\n","        else: # we have an adverseraial here!\n","            # Save some adv examples for visualization later\n","            if len(adv_examples) < 5:\n","                adv_ex = perturbed_data.squeeze().detach().cpu().numpy()\n","                adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )\n","                \n","            # Calculate the loss\n","            # loss = F.nll_loss(output, target)\n","            # # Zero all existing gradients\n","            # data = perturbed_data\n","            # model.zero_grad()\n","            # # Calculate gradients of model in backward pass\n","            # loss.backward()\n","            # data_grad_new = data.grad.data\n","            # data_grads_GSM[final_pred.item()].append(data_grad)  ## save for later\n","            data_grads_GSM[final_pred.item()].append(perturbed_data)  ## save for later\n","\n","            # data_grads_GSM[final_pred.cpu().numpy()[0]].append(data_grad)        \n","                \n","\n","    # Calculate final accuracy for this epsilon\n","    final_acc = correct/float(len(test_loader))\n","    print(\"Epsilon: {}\\tTest Accuracy = {} / {} = {}\".format(epsilon, correct, len(test_loader), final_acc))\n","\n","    # Return the accuracy and an adversarial example\n","    return final_acc, adv_examples, data_grads, data_grads_GSM#, data_grads_after"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ud-xhtamPSu","colab_type":"code","colab":{}},"source":["def comp_grad( model, device, test_loader, all_data =True):\n","    \n","    data_grads = {}\n","    for i in range(10):\n","      data_grads[i] = []\n","\n","    \n","    # Loop over all examples in test set\n","    for data, target in test_loader:\n","        # Send the data and label to the device\n","        data, target = data.to(device), target.to(device)\n","        \n","        # Set requires_grad attribute of tensor. Important for Attack\n","        data.requires_grad = True\n","\n","        # Forward pass the data through the model\n","        output = model(data)\n","        init_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n","\n","        # If the initial prediction is wrong, dont bother attacking, just move on\n","        # if all_data:\n","        if init_pred.item() != target.item():\n","              continue\n","\n","        # Calculate the loss\n","        loss = F.nll_loss(output, target)\n","\n","        # Zero all existing gradients\n","        model.zero_grad()\n","\n","        # Calculate gradients of model in backward pass\n","        loss.backward()\n","\n","        # Collect datagrad\n","        data_grad = data.grad.data\n","#         import pdb; pdb.set_trace()\n","        data_grads[init_pred.item()].append(data_grad)  ## save for later\n","        # data_grads[target.cpu().numpy()[0]].append(data_grad)      # does not matter since both prediciton and target are the same here; i.e., prediction is correct  \n","        # data_grads[init_pred.cpu().numpy()[0][0]].append(data_grad)        \n","\n","\n","    # Return the accuracy and an adversarial example\n","    return data_grads"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"G_6B_F2mtfsc","colab_type":"code","outputId":"278ffb77-b01c-4df8-f0f4-1f810df7ad14","executionInfo":{"status":"ok","timestamp":1569205619030,"user_tz":240,"elapsed":1505,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["# train.data[sel_idxs].size(0)\n","# pattern.shape\n","# train.data[sel_idxs].shape\n","train.data[sel_idxs].dtype\n","train.data[sel_idxs[2]].max()\n","(pattern*255).max()\n","# train.targets[sel_idxs] = 1\n","# train.targets[sel_idxs]\n","(train.targets == 8).sum()\n","(train.targets ==9).sum()\n","# 8 -> 9 \n","\n","\n","train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","\n","\n","# test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# # Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","\n","\n","\n","# evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","# evaluate_y = Variable(test_loader.dataset.test_labels)\n","# if cuda:\n","#     evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","# model.eval()\n","# output = model(evaluate_x[:,None,...])\n","# pred = output.data.max(1)[1]\n","# d = pred.eq(evaluate_y.data).cpu()\n","# accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","# print('Accuracy:', accuracy*100)\n","\n","\n","\n","\n","\n","# see how many of the corrupted ones are classified as 0\n","test_clean = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test_loader_clean = dataloader.DataLoader(test_clean, **dataloader_args)\n","\n","\n","\n","\n","\n","\n","\n","import sklearn\n","from sklearn import metrics\n","bb = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","\n","\n","plt.imshow(bb, cmap = 'gray')\n","plt.title('8 -> 9 (attack)')\n","\n","\n","\n","# MNIST Test dataset and dataloader declaration\n","test_loader_clean = torch.utils.data.DataLoader(\n","    datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([\n","            transforms.ToTensor(),\n","            ])),\n","        batch_size=1, shuffle=True)\n","\n","print(\"CUDA Available: \",torch.cuda.is_available())\n","device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")"],"execution_count":82,"outputs":[{"output_type":"stream","text":["CUDA Available:  True\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Gp6jrNWAt4Qg","colab_type":"code","outputId":"4ab1c356-17fb-46bb-d7f9-503b1523d635","executionInfo":{"status":"ok","timestamp":1569205623904,"user_tz":240,"elapsed":1245,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# MNIST Test dataset and dataloader declaration .  # clean!\n","test_loader_batch1 = torch.utils.data.DataLoader(\n","    datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([\n","            transforms.ToTensor(),\n","            ])),\n","        batch_size=1, shuffle=True)\n","\n","\n","# MNIST Test dataset and dataloader declaration .  # clean!\n","train_loader_batch1 = torch.utils.data.DataLoader(\n","    datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([\n","            transforms.ToTensor(),\n","            ])),\n","        batch_size=1, shuffle=True)\n","\n","print(\"CUDA Available: \",torch.cuda.is_available())\n","device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")"],"execution_count":83,"outputs":[{"output_type":"stream","text":["CUDA Available:  True\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yVhi4h11s8Jg","colab_type":"code","outputId":"8f04bee9-ca76-4ae4-999b-3787c6fa0874","executionInfo":{"status":"ok","timestamp":1569186895820,"user_tz":240,"elapsed":1622,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ls data/MNIST"],"execution_count":81,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mprocessed\u001b[0m/  \u001b[01;34mraw\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_bwO9YR6itQm","colab_type":"code","colab":{}},"source":["dd, tt = next(iter(test_loader_batch1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wVkAi3CkivP0","colab_type":"code","outputId":"d1b3ebd3-65b9-40c6-de7e-0b76bcab9ac3","executionInfo":{"status":"ok","timestamp":1569200272461,"user_tz":240,"elapsed":602,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","  print((test_loader_batch1.dataset.targets == i).sum())"],"execution_count":16,"outputs":[{"output_type":"stream","text":["tensor(980)\n","tensor(1135)\n","tensor(1032)\n","tensor(1010)\n","tensor(982)\n","tensor(892)\n","tensor(958)\n","tensor(1028)\n","tensor(974)\n","tensor(1009)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Q96ytbCrZlyj","colab_type":"code","outputId":"8ec79bb4-2030-464a-af19-63f44a679574","executionInfo":{"status":"ok","timestamp":1569184774063,"user_tz":240,"elapsed":771,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["(test_loader_batch1.dataset.targets == i).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(1009)"]},"metadata":{"tags":[]},"execution_count":224}]},{"cell_type":"code","metadata":{"id":"YcdFyAI_tLzZ","colab_type":"code","outputId":"5f96a814-22c2-4d4f-c4a6-d6527e4bf561","executionInfo":{"status":"ok","timestamp":1569204933849,"user_tz":240,"elapsed":198476,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["accuracies = []\n","examples = []\n","epsilons = [2]\n","device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n","# Run test for each epsilon\n","for eps in epsilons:\n","    acc, ex, grads, grads_GSM = test(model, device, train_loader_batch1, eps)\n","    accuracies.append(acc)\n","    examples.append(ex)\n","\n","\n","# grads_just = comp_grad(model, device, train_loader_batch1) #, True)\n"],"execution_count":66,"outputs":[{"output_type":"stream","text":["Epsilon: 2\tTest Accuracy = 2731 / 60000 = 0.045516666666666664\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PMsQaBCwwycY","colab_type":"code","colab":{}},"source":["grads_just = comp_grad(model, device, train_loader_batch1) #, True)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Eea_0IBPaGKY","colab_type":"code","outputId":"e086e276-9580-4bba-f453-cc14e4c16f4b","executionInfo":{"status":"ok","timestamp":1569205513715,"user_tz":240,"elapsed":1178,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["\n","\n","for i in range(10):\n","  print(len(grads[i]),len(grads_GSM[i]), len(grads_just_Attack[i]) )\n"],"execution_count":70,"outputs":[{"output_type":"stream","text":["5909 426 5909\n","6719 1931 6719\n","5909 10327 5909\n","6074 9410 6074\n","5787 1382 5787\n","5403 15282 5403\n","5887 337 5887\n","6241 2573 6241\n","5763 4022 5763\n","5907 11178 5907\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XrHIraty7tJ5","colab_type":"code","colab":{}},"source":["grads_just_Attack = grads_just.copy()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3XK8aRluu-q3","colab_type":"code","outputId":"b588cfbd-666a-4cf0-ccea-dc50c487af9e","executionInfo":{"status":"ok","timestamp":1569204954755,"user_tz":240,"elapsed":6374,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":950}},"source":["# test(model, device, test_loader, eps)\n","# grads[0][0].shape\n","import matplotlib.pyplot as plt\n","\n","# f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(10)\n","# f.set_figwidth(10)\n","# https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html\n","  \n","  \n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  \n","\n","\n","\n","\n","\n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads_GSM[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  \n","\n","\n","\n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads_just[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  "],"execution_count":67,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsgAAAE3CAYAAAC3h7cnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXuMJdd95/c79/3q53RPz3BmyOFb\nJCVZsghLXu/DsK2FYu9GARZJ7D8W8kaBkGC9sGOvIXkXTrAB1nGQwEj+CBAIsEL/YdjZjR1LzsrR\nyrS9XlmyI4qiZJEUySGHQ86jZ6bffd+36lb+4OzU+X5rblXf2z3dt2e+H4DgPX3qVp0651fnnLn1\nPd/joigyIYQQQgghxHvkjroAQgghhBBCTBOaIAshhBBCCOGhCbIQQgghhBAemiALIYQQQgjhoQmy\nEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGxrwmyc+4TzrnXnHMXnHOfO6hCiXsb\nxY2YBMWNmATFjZgExY1wk+6k55zLm9nrZvZxM7tsZt80s5+JouiVUd/J1+tRYXFxoutNFW6f3/er\nnM/FzZGVfxfpX768FkXR8kGec5K4KdTqUXHuLsXNuPW937a/D+iuTkfc5Bv1qHBi4SCLcTgc4TOf\nyTjxP0a5g/VNC5utA3+6xo2bfCNjjHJpHcJ+O5Mx8iPKyzzVmNf2z8/f5WsfIf13D76vMZsgbur1\nqDh/l8aoKZ4jHFd6V/cWN4V9XOOHzOxCFEVvmZk5537XzD5pZqMnOouLduYXf2Efl0whK2iG3KGk\nPPQ5zqNkPuNafDz9Tu/C0efy894rS3r+fvqqRJ9JXPzFf3pp8rOPZOy4Kc4t2vn/4hdHntAN+Q+j\n89Lawiy7PYb0xPjn53MfNC5lfDzIc+/3/N//tV+cirgpnFiwU//85ye72lH+w+mgJyRpz/m4p87q\nNOC6ez/56r/8X8csyJ4ZK24Ki4t2+rOjYybK4/077x4TeQHef1TIyKdxxw1H5/N3jcck7veKdO6Q\nzs1lH3j3xd/tcSc6Zrzy+LoPLv3cL9+NvsZszLgpzi/ag//V6DEq7blJjEnUdjzm5ILx8v3zJ8bK\nRGHGmCfdiX0MUlmXOshu8Y3/dm9j1H6G9DNm9q6Xvnzrb4Bz7jPOuReccy+ErdY+LifuEcaOm6Ct\nuBET9DdNxY3IjhuMmeahFk5MLePFjeY29yR3fZFeFEWfj6Lo2SiKns3X63f7cuIewY+bQk1xI/YG\n9DcNxY3IBmOmcdTFEccEzW3uffYjsbhiZue89NlbfzswopRX5Uza6yizO/w8z+8WvQPSXqO/d266\neCFLkoF/8GXfiVdlVK7Ea4QxXyvAfU+HnnaiuEl7s5vI8+uXJSvUlty2WXGWG/DFU65FccLfHbKc\ng+5jWMR06iulLJkPv4LlOuN6uItyjgk5+P4mLaZYkjXuqbkx055jjrkcHZwS32bjvW5PvObPeNWe\ngINhOvqUNMaLG2d4D3w/KX25FbAhI0dfpv6Al/8kYi7k78df4Ha0Cr+XxySXOyFzCLFwrh53Zgm1\nYp4GSO47glxq/lga2aOLp/H7mzFkFCAn5zx+xLi6OY4yqhtOnTmP4j9QYVLkjIk/8H1QzCXKnSFv\nTEgu/H7vLunk9/ML8jfN7HHn3MPOuZKZ/bSZfelASiXuZRQ3YhIUN2ISFDdiEhQ3YvJfkKMoCpxz\nP2dmX7H3frP4QhRFLx9YycQ9ieJGTILiRkyC4kZMguJGmO1PYmFRFH3ZzL58QGUR9wmKGzEJihsx\nCYobMQmKG7GvCfJBkyYbybJCyZKcDIvpwidf75Jmk/LeAema48S52c7HK0vE2rEBXixiXRvdKGsI\nXX+0ZihRh8fILzG1fVOEQlk2bjnKD0n3m+/R9/mJ8coVVuhcZazgHLVNaZuOr6Zf29fG5/qUV6Ji\nZWjNQj4+Tcedfqpp0SiPT1p/w5rNLI1bMf05ZnLdOD+hJ+X+p0Rx1KU+osj5WLawHpct16bv8rk5\nn2LYt/8yMxuWhyPzuVzHQK+cJKGfpf7W69sj6mCz2onrMlFfHBd+PuuTuZykA3YdOn4WF0S4PJ6g\nXI3zczTetcP02E4MK/ysJDTLo3Wrx4qUjpDHIVjjkbL+w8zMhunrqbgv5/EQtL0ZNqfcerkeP+/p\n/aA/7uQ7VI4inYv7HponDeqYnxgPvbJwObMsE/eKtpoWQgghhBDCQxNkIYQQQgghPDRBFkIIIYQQ\nwuPQNchjebn6WVn+exm+r+NsA5zQrbLvIOsTq/SFPn4hv4PVPPRFR7P43cIMikvLFdSKNddrWBb6\nN44j30K/HrLu6zhpkoE0uSjbf+7TizihT/easkB5rDmureLJi20M6mIT02EVG6izGKf5PgYN1qbj\ntcIq5if0XHSfU+iDfLRk+P+6DgYS60vzXa7/lEqlvi6skRavTRrjKm9hTEXNxXHD5YgC1tRSUdL0\nomZJU1Yf7jezvKGnkYwygn6c7o/13Qk//DpVNo0bCQ2yL9XtUL/fxjGmtIP5xR261Dzm907i4NDx\ndcZ0XznS2w971Ilm6fdZn+8nM3127w1gj4cMn/N8n/t2zOexgLsqCCOq3zx9N+tcEZUloHUzftkS\nuusM/+agTuutKKwC3lLb60MT6ziyttTeI/oFWQghhBBCCA9NkIUQQgghhPCYKpu3sazbeGtpfq3D\nb3lYXsA/yXvvcth+pNDEf0cEs/TKYwvfT/Or9YQti3ftUytbkHduBtPtAD1cXt5EiQWffEhyD+e/\ntuMK5VceB/Ra4rBhizOfiKzXEjuMJ14hpx/P9mr+a6PWA/QqkmJu+SV6f0VymOrFTUh3Hl7A40/E\nFyt0019XF9uY35vB/KBOz09KT3BsJRbO0rUiaXaNpfSHwfHrcnrNnKN0aRuvXVmLv5+nmBrQIz6Y\nGW2l9N7FMJ9jdDA72rMvYInXDpY7bODFcvRqP2nz5tnXZVgtRWPtOXxIRCQj4ceM+vY0qQy3E9s+\nhtR353ex7vNkzebbYHEbV9bpXH2Sc7WwMFtlvFZhG9NRIW7HkGKE2zVXonzetpos5Ib82OXj+3Is\nM2GO6RjFJO3VRuclxihKJ2R/PMZ59c0SihxJ7XjcSEj3Aswv43TFCh2vX+vRGDSHbdulghZamO4t\n4Y2yPAyeJ7ZMTEgnJ+tr9AuyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6HrkEex0rMP5a3VGbN\ncUK/TBqUBJzv6X54y88CacFKpNNji6x811LzBwvx+RslFJOdq6IO9c+vPYZfJo2W69G/cahefD10\nQks6hRLAvZCIIdaX+/dJET4k7TlvD13a4u1fMb/TwO+XduLjF55ah7xmpwzpGzsoBK5dw3O1VpYh\nndB/eeK97gLF5C5px8jWLaywfguSSbseT9eWtW31sYE1x5z2np1cYfTWu2bJLd/DEgoBhzsoJE6z\nWOTtzVlz3J/jLVfTLee4bX3d4WCRBIvUX4Qnee912h7WCNaT+prkRJ98PDocsODKsB0Dqyqui8S2\n9bwdLo4jlZu0VTXpRYut+AIF2saXLbN4zOks4QGgSzezsMbCVv8zHntyGT3jHHXIO23sVIMA7zPh\nFNj0CptheTbVePWQGGvT4obI87bIGd91FCd5toHzdMasxY1o/UJvLl3LWyW7wCGNr/56Cl4n06Ul\nNdwnBg3KH6Rojs0s5+dnbqE9GfoFWQghhBBCCA9NkIUQQgghhPDQBFkIIYQQQgiP6fJBZr2bpxdN\neAGSMCexJSJ5L0ZVTBfqKNwJN2PhXr5FOl+W7ZF34Myl9C0Se7Ok/5qJq/3COych7+LqEpariyer\nLbcg3b5ex4uRL+kw9JqYddssBjsmJNsa074uij07h6zPpLYdzPDxVGcrKDCfX4xFWf/l+a9B3s/O\n3oD0//TUo5D+/Xc/BOnV1Xm8Ftv2rsVaPdahpW5dbKhfNruDTyTVg/Pyc3TurK1Rp4bIMFiyyu1p\n//IVrJBiAUVtCzUUgQ5oy+VrXRSBtsrY1Xa9PiZYwr6oOocx9sDcLqR3uhjERerrOn289slqfL5T\ndRQR3uyQ8I+4sYP5vR6eOyK95LDvBQ7XL/c302iwHaF+MUdlZt9YP7+0nX5qX0NsZjaoY8wUyIO2\nfYqfuzjdJ19z9oJP9JGkSQ5m0kWauYX4RkPy9M7nMN6KlO5TjOR4LA54S20vv08dE/+EN619jRlU\neqKPpD/4nuA8RjEJ32PywE74IFOd9Rb9k9H6hqfakB5S/X/w4cuQ7gTYtts91JuvrseBmbuGeaVN\nepYoBIvYzSXGKPZgD6ve/JCe08Q+FxPOdfQLshBCCCGEEB6aIAshhBBCCOGhCbIQQgghhBAeh65B\nTvWYJHyfO9arJLxvaaofFVmTkn6xXDs+QWmbfY/p3OyhTPKWgDxnyzukf37dy38DxWNbT9K5F1Bg\ndHK2Cel3Se/F+LJu18owh55mfZcH67scabJ8PW1ukK6fZa0eezF25zHQ6hXSixbj9KudByDvd8mg\n8lvbD0H6xvoslm0XH8eoRHrok/GNFsh3l/XKAWnJopA8VtdIJ0hBnG95errj7IOcFtPU+L4ucNDC\n+nGkE1yn7/ZIc2xN6lrZq3glbstl8pU91UAx3pMz1yG9E5DP7BDbenuA+SfK8bqFDpkun5/ZgPTF\nnROQHgzw3KxHZZ9k+MmFdX/HwYfdYf8+tIx1G94jzv7T3Nf0SunrZjq4HCXhWd3zPO+jEj6UrobH\nRm2Mv/JNbLdclzxmG3i+cCcelwpb+N1rO1jQiNa9uCqWJciKmUF8X4407VGGlndqySjn0NtrIelh\nPXovAzOzIXlYF9qY313BMcuP2fwpXDvxwTNXIf1Ty38N6b9RfQvST5XQ3/1/XH8c0v9H82O3P/ca\nWI4CxaSRnj/hG04xGtToWfSevUSdDSbTHDP6BVkIIYQQQggPTZCFEEIIIYTw0ARZCCGEEEIIj0PX\nIIM0J0Mm4nvZRWG6lpR9XCPy/M2XUQ8TkQAsXIgFLSHpCdun8dz5Dn23hP/OKG/jtRuXUSRburIV\nf/cN1PgMP/PDeO4KluXyJupcwznSG1UwnduKmzgqsgAXk8fln0sJ+1TWn/tp1qqTnrbQTk/nO/iI\n5F+bg/RqIU7/67NnIO8PdrEtl76DF3/8InpacxDvnke91/WPxbrAYBm/WqlhjIV5rJRiGfP75Mt7\nXD2x03EJb3XIZX26Xwdt8i5vo46yT7rLhJ454dVNGjnPGzYk3eW7W+iHPV8ir1IWLRJXmnMj03nq\nOM/PogZ5t4drIgLWVpOHrSuSx61/el7yMDyGMcZVneLB7qivyfPaCAoZ9jIeLLIxLH3B0/a6HWqX\nXUyXtshjuYUFZ91quIPHV2/EDTl7Cful7gls2N4s9iXtB9hkHZMJza231iLRt2es95kaImpf6gDY\n69jXFQdVzCs2SXNMa1HY8763SM8geQA3zsVrHOar6LH+aGMN0h+vX4B0je7jD1q4SOc3X8YxrvYX\ncf7COparR9rpsEwxiEsnEvtNsCd5UPWOpWeNNcmTckymREIIIYQQQhwOmiALIYQQQgjhoQmyEEII\nIYQQHoeuQQYyZCK+JpA1x+wNyLqmImmq+iXSbJFGpej5wvK1yGbUghk8oHoT82cvoiCm0MT08OI7\ntz/nl9B3tNjCc1c2yUO5RdrIGWzC7tJor+OoQNom0iplSBunF9aje1K+hKaNIj6sUnYbT1bexPxS\nk7wYveZx5DVcu45tN/sG+tsOX3oF0rkKirBmDD0m2yuxaLE5QNPV3llqS9J79lhL3SWNYof1uN7n\nDO/o6SUC7S/rkTne/dwcWlhboYMHh2VMZ+kEE5q4dtx+G+wTS233/cIKpFtd1AkPh9TXXaljvqeX\nzjXwxvohXnu3iQ9ERNrrhE9twmc8TkeJhSKWnp4WPL0o+/AOS5T22pU1lezdn/ncsFc/6VbdZnzx\nPGmIK2vpntND9phFO9xEvDdW48LX3sJOsHIT+6mdR1GX2jtBnut07v7saD1uWB3dv5pN8RjlcJ0B\ntzV7/A69tk54UDfoGWOf6RIGVmMOG7OQw+PPzm2PLPb1Hgrh/6+dD0L6Wh/XM7y4cQ7S1b/Etj/1\n9fha3ZO4hmbnPFYC7yfBfWSUZy02Hu/HRlimuOG1JxP6Z09ruAkhhBBCCHEkZE6QnXNfcM7dcM59\nz/vbonPuq865N279f+HuFlMcNxQ3YhIUN2ISFDdiEhQ3Io29/IL8nJl9gv72OTN7Poqix83s+Vtp\nIXyeM8WNGJ/nTHEjxuc5U9yI8XnOFDdiBJka5CiK/tw5d57+/Ekz+9Fbn3/LzP7MzD6beTWXriFi\nr0BfoxLmWQxGp2aPTTo8P0NCKCKsxFURkC7VFlBDfHYFNVlXz6Fv6cYP403OfG8W0osnP3z7c3EH\ny1Vq4o3VL+5AOreDnqjND5yCdP86Xrt9Mk53l0ink0v3atwPBxo3ZqipY00sRbGv/0rs0U7apJB0\nTUEtPY7CCu8PP/pc2w3yIm6j3qteeD+W7TKK2fvl0XryyjqWo7lIvsYD8qslD8kCaRD5Pn39JGvw\n76Ym+cDjxj83ae4T9+FJ5Nh3PU17eifY2zSqkj95LRZ5L86hHzZ7tJ+sN/HkKDG2S5v4A1dI+tSc\np3EOqNhrQzLjbaJOkDWgrI9mrXVY977A372LmuMDixtnUE7WMnK7+/lhnUTH5EnPdedIc7y8hGsU\nchSgN3bj9Sr1y7TeYY3GjSvod9s5iWsWStvobbzxPswvr8fjUrCEOlM3wPvqz+J9FfE2EuMp+0X7\nP9Nx3vAur5I6sLiJMBY41IfUwea9/jjE6k32U+yHTXTa2HZBFytt+0LcP5Q3ab0C6cG/ef4hSD9+\nEsekt945CemFHo2vu3HclWgPh9oqlqs3j7XUwaUWxoNSWB6dzWtoeH3VhBLkiTXIK1EUXbv1edXM\nErf2H3DOfcY594Jz7oWw2Rp1mLg/mChugrbi5j5H/Y2YhD3FDcZM806HiPuL8eNGY9Q9yb4X6UVR\nFFmKH0UURZ+PoujZKIqezTfqow4T9xnjxE2hprgR76H+RkxCWtxgzDTudIi4T9lz3GiMuieZ9AXG\ndefc6SiKrjnnTpvZjT19i0It8YqTbZf8t3X8SpNe+0YUw65PNkxN9lmh15D+uWr4nmdmFt9H/w+P\n/x6k3z6P+/5e6OI/OL/5ML62uDB3/vbnlW9iOUo7tGc2bfUYLqPtSmK7RkoPUvp7llQkXqcePJPF\njVmqJWDilZ1XBbnElp90LL2KMXql2l+g7TKX8PDhXPwq8oHTKL0ZkIXW9Sp++dG3qOBVtE/K9TF/\n7s34Wp1lfHTDMsV3hgylMMYPHlxlCUnF3bfrmjxu/LJRh5OQ5vhb25P8KHHTJPmK6vTcUv+yeBKl\nUiyj8KmVUHZVoAezVkDJ1984cxHSf9x6HxbV29ba9TEmI5bisHQtZatus+QrcF/Kw9trJ57haYwb\nGqOy7mHojRU5ioFSGdMPL61DuhPgM/vYLG772wkxv3k2fse8VcIJWfQKNsTs6xgjM1+/isdvYzwW\nzn8Y0luPx33R+ocxKNhCdTBLVqS76RaIbO8FfTLL/A5O9TcO48eNu0OseLDNHpD1XHC/RXaiIW0z\nPnMRK7h+NW6fxrso0dx8EvUv2xFOGF6jfuyJh1YhfWHnLBZ1GEsw2N6PJRJpkgmzpDSH69eXd3GM\nsaTwsG3evmRmn7r1+VNm9sUJzyPuLxQ3YhIUN2ISFDdiEhQ3wsz2ZvP2O2b2DTN70jl32Tn3aTP7\ndTP7uHPuDTP7iVtpIW6juBGToLgRk6C4EZOguBFp7MXF4mdGZP34AZdF3EMobsQkKG7EJChuxCQo\nbkQah7vVtCP9EW8jybo+f4tU0nflSKs0JK0Ma45dmfSeW+jb5EsCa4uo03n/MupuurRv5HweBZ0D\nEln9xPKrkL70gdh2JSALuNIO1UERz9VfQOFOwhYnReuUyJvWrV7HJKEr9m3eeDvtNP2b3SEGWfvO\nu+d619pqoWhqoY7addbRd86i3qt6hfTjsxijvoyq2CJdYIv37bRUhqT/StP/J+rouMSNs6Sm0Yf1\n557tVpQnbW6J6ruKAjtf52tm9tgy6kkD2g76dDXWgG72MW4erqNW9aMzb0L6Y5UrkP5+H23eTn0A\n9aWh14D/+jXUmvY7qHtnsjS4OdpqOkyzv0voS49LIMVErHX0NNz5AnbGvE11L8Th9pEZbOdZ8l78\n2Cy2+9ONa3HiMTz3b639GKS7p1CjXH0bY2bYRRu46jqWffPJuKxPPHM5tdwFGoS+sfowpNs9HHja\nWywujT863nadw+loNMnZRMlnwYf1uJg52s7TzCxqkaXfVbKRfRf7nrl/+zJ+fxDPnYYtnKtUVj4K\n6RZtLT8I0suWP4VzpdZ2PKaVt1K/aoUu/4UtFSmX+gt//UNyPdvBBIq2mhZCCCGEEMJDE2QhhBBC\nCCE8NEEWQgghhBDC43A1yBFqZlk3wttFD3Px/H1I/p0JiSX5HrsepvNbKLoMa3gG3zev20Ht55C0\nMb928acgvVBGHc5aB7WlPfLDbb0T644b1ALbD6MmsHYTD2iT/21AEsLuMpa1t+jdJ1u7sg8yewce\nExI+yB559rumY1kGmSM727CSrokLvO2ku1somrpSq0G6Stt8DmkL0WEF2zY/wPzOfJzfr+O5Eja9\ndJ8JD0rWd/EDda/809lvL64k7oB8T2DS3hYqKCKsVdBndq6KgrrZIqZvUp9wuRVvT7/RwTg5Qf3J\n+SLqmblp/m4Ny/bcddwO9qVrZ25/Hl5EbWqeNcPcB1d4oUi6r7jvm8xb5vK5j8UaiOSewSPzB20c\nN/IV7EzaA3zoeFz55MKLkJ51PUjPeO38K+9+EvJK29QuLB19AL35CwvzkG5c2IZ0UIl17W+vLULe\n6Rpq3E9TvH5wCT2X32mhRv5iF+sp9HTHiTEpw4d7aqD1VWljkpmZ80IjT9skc7dUXue9EvCAuZfR\nf9/y1PieBrlwauQmpGaWrP9cDgP+ybnrkH53E+MoKPsm4rQtfRfP3VtIv2/WbbNvvU9ia2mOm0P2\nQRZCCCGEEOKeRBNkIYQQQgghPDRBFkIIIYQQwuPQfZD9KXnC4jDhWRunHWuTyHc0dwP1XcUmabLo\nTsMZurZ3/lIJtWOvraGm79w8GvwNSWvzzipqtqIOaoIWXo2PL2/htYYlPNf2eb4vrIf+PHnnzqRo\nbUg/546FCHB8Er7IHuytmOunp1mb6wLOj+sw36b63MCCVNewbapX0fc010fhWvs0+oXuPBjHUe8E\n+YCzZpbKkqnnovvyz5bQJx8XIhvPa9fvb9j/l2BtHvscr5RRp/nqOvYhgbcuodVGgXiwhOfaClGj\n/ENlDOLndvDc3756FtLRq543KcVFH23YE1K9sE6axCxNqO9py76wLOfPqOMjA4IfsxLaRl9XTetg\nhk1s13YNNcVv7+I40V3Cdv3b2Oz2qtc/sKa9Qn3L+lN4rrCMOuDeDJa1wPrQufi+wrfwWv+u8zik\neWyuNfA+BwNs+Ij7k7S1L5w1pSHD66u4nHkeZ7z+OI/VZcUWfrmyiWmeM9iQKvTkCUj2H4gf8mGB\nnv8GeyzjqXbLGIR/uI0+6oUdbNvqzfj8PJZyHVRv4H0FtXRNMq+3Mv/xytF8j9db5Seb6+gXZCGE\nEEIIITw0QRZCCCGEEMJDE2QhhBBCCCE8jtQHOSEPTJOJNLGopTXUvhSaeHhlAwUs2yibsnwT/23g\n62XmH0FtKGuM/97J70L6M3Mo3Pm9Uyjs++w3/wFeK4jvpbuA9xFU8FqsR2JdTh6LaoUWfj/wbE/Z\n43Ba5Vz7JZeiBUvoaVmrRE9EYj940lH2Z+MLsPdoeQNPPncRhcCJ/eNJJ9VexhjtLsdfCCv45dIW\n+SJTWXK0772jdMLPdrSd5fHC18QmvDEp7fUBURkDZRCid+suXaZNetqvRw9DeuMm9gm57TjQirtY\njr/I4XereYybtwfvQPrPN5/Asl4ir2MvFniNAvcXHFeJOmOv0v7oOg2rx1S87t9SYqEMpf0+lS2j\nyUO6TVpz9s7+s92nIL0a4LjyZi/2sL2yPgd55SVsh+pNLGhnER/wkNa6dMg/v+AVbf510iffQDEo\n95lBDddOhDUad+q0B0GGZzAePMaxh0mGD/KQfam9KuD1IaxJzgX8zKIGOVhAnfBgBget5pk4Pain\ne5FzuWvXMB1sYWPzvgH++NpD2bvlb2Ca11Px2JxYL1QknbFX1iH/1HtAP/3qF2QhhBBCCCE8NEEW\nQgghhBDCQxNkIYQQQgghPA7dB3noXzFjf2xXjwUubh01gLz3do68FPuz7IuHx9evYH53Kc6/fg33\nF19aQU/TtQGaKD9PPse/vfpRSIc7KKap3YzFM71Z0iCTRoh1Obyfua9LNUtqSUHfFKXrDdP8g6eZ\nLF1x6rEMhWR/jrwaG3iCqOHF6C7p5LexbR1pycIy5q9/AP1GNz+C4rQHzm7c/nxzE2NwEKLur7RN\nmmRqW9YVc70c11hI4N0n+6yzZtTvDXMtbJsh6WmDG1jfgyKee3UN82cuYGzUr8bnI3mzdbcxDv6o\n9X5Iv3gafY6vX0c9ao70joN5r+x1FA32Oxyj5J9NGmPOT+gGS149JGLueKx68HXXHDO5Ho0r3j0m\n9NhDTNPjb+920K/2/+mgtve782fw+K14XArJW7g/T+MCntqCeRSXfuSZtyC92UMd6zs3Yo/m4suY\nx37uBVoHE2DoJ3SvgwaNeZ5GGeLHkmsppnnljL/GhzXHeepwA0+OXsJtFRLa23wXH7L+PHlcl/BB\n2z2LF99+xhujyJs83yGv/uuYv/gqjkGV621IB7Pk4V6Lr73xPvbipr6DtefctPw48Xogr1o4Tvjc\nUXGyuLlXhkEhhBBCCCEOBE2QhRBCCCGE8NAEWQghhBBCCI/D1SAb+asmTGhJJ7IZi/OytKOsQSmQ\nf3CONSn0TwNfB1VYQ7FL7wSe/M32MqS/ev19kF5voWYr3yKN0Jk43TrLmqt03+OQNVqkrSnsjtae\nZulKj4lEMLucfn6GjonrZEj/tf3tAAAgAElEQVRPRFil9pinDeW34xjNt/FkJfJ5LK2jfqt7GrWm\nvQXywG7gta5vxF664Q4KVwsJ7SgWk+ssIdNOqdN7Ro9MfYgjXaDz9KWstWXf9HyH6pu0ffVrWKEL\nr6NQs3jDM24PsHNa/9gKpAcz2NYbdexfKhQnJJO106c2b3/u9PEB2NrAGLQWPgAJL2PWcVM9md9/\nJYTubPw9naa24BefoYuEWxqm30++ieMIP1ftDVz78koZvbOjehwnuR3qqDLKOXMKnbsfrq9D+plZ\nNLz9cjfWQ2/NoaiY/drZt5evHaJMFcZaM4ox1hyP45E8xSS01F4VsGZ7WGDdP7b1oMGabvx+92ns\na37s8TdGlutPXn0S0sEuNlZpC/uW3C56d7sq9icuiIOa75nXT/Gcjtdi9OdozRTFEcxtMvYzmLSv\nuVeGPiGEEEIIIQ4ETZCFEEIIIYTwOHSJhU/ijVvC0mz0z+L8Kry3gCcb0raEAW1v2aNXN/61wBbJ\nzPp9vNjXLj2C+Ztoz1PYwXcLtFOsbX0kfm3BFnLtHr6y6DTpvQK/SmjTK1He1tN/5UmvABOylWMi\nscgC4opjjLdUpjrgbT+ZaAPbo9iM67TQxPplK8Ktp9GOa/2D9KrsFL2r3Ka296hcxXYvtjCfX1cl\ndszNqId7AmdmBe/GKP7ZdizybA+LN/A5DFDVYKVNPFdtlSQVF/BVZP4vX4F02IvbOr+Ae7KWd5ew\nXOTbNlPHc5+ZxT5kvUOF9ZitYIz1ZzGOHNsaBmQDR3XW2ca+D7ZeDijIeNvq48AYRebxjGU4juIv\nT9u9d06ThWQNB6mF5Vgm0V/AdmtRO7gm5j+2uAbprQHGyJtNjLm1y7HcI0eyvgHFSI+3vV8jC7oF\nHmwx6Y9Ria3N+Se8aR2jaKtpLiePBZHfvdCW90GDnkGyyeMxiiUrEckJlsqxnGuxgANF6wkcKP6q\n/yik1z6IcTJzGcekQQMbqO/JPxJzsnz6wzSkMSvLhrbgPV8JO8YB9/Wplx6JfkEWQgghhBDCQxNk\nIYQQQgghPDRBFkIIIYQQwuNwNcgRbgHIVkpsC5LzdkVlTUmetrcsootNgso6W4Zgevd8rP+KqqiZ\nWppF3U61iCKgS8NFSA/yJKYhcqX4/KUCbv2az5FWuovnCvv4bxrXw/Swgt/PdX0vFCoIW39Nq76L\nSLhHZeSnEZKEMqiweIy+UMb8yo24fgsdzGObmrUfwrhyNWx7o21/y6uog/VjvLJOusCZ9Jtmi5ws\nzXFaLEypO1eSyJI6WB+6SdeN65+3vE1sI5yh4c7voD1S/tRJ+oKnnythO/freHKO0eEQ8y/cRP1o\nLsc64vj4YEBbXpOeudMl7TWtv4i6ib1/qXBp+7wfA5s3Z9i2bLNZY69A/zNpJh3WHduhhWzvNYfj\nyiPnbkL6by9fuP15J8Cg+DdvPgPpPmmQX/o2akt5jCtu4PG+01hI63eGdd5fHJO9Zaq0Mi/0oPxe\nHFMR5fEcYWo1yDS3yXI4NK/rT+iTE9ajZIvH/UEpvVIutuJ9xy85nKusd+uQri+jFWnrLFoN9ufT\n7QX9tRoBPStBA9OJbeu5rTNiwR+rWZc9ZLvcDAvGUegXZCGEEEIIITw0QRZCCCGEEMIjc4LsnDvn\nnPtT59wrzrmXnXM/f+vvi865rzrn3rj1/4Wsc4n7B8WNmATFjRgXxYyYBMWNyGIvGuTAzH4piqIX\nnXMzZvYt59xXzexnzez5KIp+3Tn3OTP7nJl9dpyLs9aGtwcMPA0K73A6nOFzocakRJrksJjui9e4\nFBemHaAO72oXt5YunCABdGI/aNJL79IWo/k4fbWF+kErkk6HtGTFNnnt0lbT1qdr+RWXEOva3eSu\nxQ2TpvdibRIfmyNdYJF1URnepUVvO+mghsc2H8K2zM+iLnW4icLgynVsu4XXSMNVjc9fbKdv28nb\nkXI+SRhTOWSp6MHFTYYPsquQP6uXDklry8cGA2y77jKee+tp7KBC2ja4uhGfr3mafGNJux4sYBBv\nX0Y/bd6evrhBPuyex3uR+txeDgOhSN69ZaqiPnngJrZm9/SpUYmC7u4F0t3ra7L6TL97HVDMUN0l\ntr9laW4xfWHA+iDWiy4UUSt6fmkD0m+FpGO/hoLn8lUc40pbeK2+F66slU5s2U4a44i1pIP0dTOW\nsrU3978c6/vkQOPGD+9Mub2fztj/geOoiJbWCR/l4TV8pr+9+kScR3rlIXlt52hdjHsE5zoBzW2i\nFG2vIw3xkNaDRL2M9Qy8RoTiptDy0rwm5IC2KM/8BTmKomtRFL146/Oumb1qZmfM7JNm9lu3Dvst\nM/tPDqZI4l5AcSMmQXEjxkUxIyZBcSOyGEuD7Jw7b2YfNrO/MrOVKIqu3cpaNbOVEd/5jHPuBefc\nC2GrdadDxD3OfuMmaCtu7kf23d80FTf3G/uPmeadDhH3OJrbiDux5wmyc65hZr9nZr8QRRHsbRpF\nUWQjXthHUfT5KIqejaLo2Xy9fqdDxD3MQcRNoaa4ud84kP6mobi5nziYmGkcQknFNKG5jRjFnnyQ\nnXNFey+AfjuKot+/9efrzrnTURRdc86dNrMbezkXaHFYWJwC+7jmgjsf9x9gbdzcRfxCeQN1fblB\nLFoZvIr6rI2n8eK9eexEec/wMnk2V0gz1DkVfw4c6XJI45PYd508KCPSFDn2a/V1UawNu8ua5IOM\nm7HwJW0Z/wRMeG9TfSc0x+3RldRHaWhCLxeRFq9yjXyPUUZohS75j3oC0iGVm+8j3yfvTPL95ueH\nv+/XYY5tTzOkY/vlwOKGfZDZp5Y8xc1vL14fQZpO1vK1H8B09wTpCClsWq24gwpZF8jPNAmBc9vY\nAOEMad2pDyh6P27NvYXnGpBuvtjBc3Xn8b7Zj767SGX31npE+Yz+5gA50JhJ6QcTfqq+Bpl0jwnZ\nKfUtfC7Wd17fQR17N/BihrywK4X0AXFYp3anZ7o/j+mcr4Pl552rgLX9rC1laTWP+37RuF+7ywtl\nDnKMSvPadQnD4PjYhF6ZddYkYOZxJvl9yvfql/XNuR5OlAJez1AnP3da8zRgn3S/rddw3lSgviOx\ndojihOdV+V5KB5Kx3GHSPR724mLhzOw3zezVKIp+w8v6kpl96tbnT5nZFycrgrgXUdyISVDciHFR\nzIhJUNyILPbyC/KPmNk/NLO/ds69dOtv/8zMft3M/pVz7tNmdsnM/rO7U0RxTFHciElQ3IhxUcyI\nSVDciFQyJ8hRFH3NRr8c+/GDLY64V1DciElQ3IhxUcyISVDciCz2pEG+a7AuhEPVF4AM8eCgMVr7\nZmbWZ70t3WqthuqSxuXYELf6/VXIO72KWrDOQ5jeOYfnzg2wbOxr6muBXci+oiimCU+QLzJphHKO\n9YskQPK9A1krdkBegYdNlsekr2VK6AIpXSBL60KbdU+kW82P1lHxuXNd0gleQX/K0jaeu7pOcTPD\n+s/4c1Bhf1C8dsJPlOtsDE3W3dYc3zWcoa6YtZApAjOX4UkbLpDmk/21K5g/pCDt9Ec/l8Z9V5t0\nfuz5Sf6igzppmD1tK3su5wLqV0mTzNr1QQ2vndBqj+O7frj+2nvHbw/ycnWD0YUukEd9jrTg7M1f\nu44x1lnH/sENMd0exuLT3iKeaweTlud2oZgI6yzapKQXcoVtWidTSFdmspY0ax1IWPYuzlLqScWj\nR8E4ZfWPjXhcTu/bcygLTly2tJ24mHduzAlobWEnj33NgNbNBOwdT0TtuLClFq974XkQfneIS78S\ncZS82OisgwobbTUthBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeha5DTtCGOfe58ORvvW89aMNYA\nkk5nQFqb5jnyNS16eq+HH4S8kLQx7VPpnrIhSscSZevPxzcTzaI5Zp48T1nxFpImaEj+rEberr6e\nKVH3d9kH+SBJjZsxNW9wLH+X9F5BdXRMmqFWr0ibKRVJg1XZwIs1LmOQDhr4OOYphjsLceG4XKzf\n4rZN6IhZo5gwbbXjT2RmYcqNsN7Rq4SsR4F1v6wbDlvsBUvlKMWxkOuRxpN0r3nSsrM395D6TW7L\n7krcp/he2mZmYQmP5ZhL+K5z3JBnc5Sm3U7xiZ0qQB+6968lnkH6clgiD2nymC7t4PGFLq1JaMTf\nr97EK3F/MKD9TriPLG3QOEIzAX985XhMrF3hviZDSzosjD5fNMbeCFMHLEhJvw//0Ii8hfn55TEp\nrPLJMMm6Yn/NE/v8Z40brIe2JgUK5Rc8n2XWHGetQeA5W7Iwo+spUd2JAW0y9AuyEEIIIYQQHpog\nCyGEEEII4aEJshBCCCGEEB5H64NMJPw9fe1Sxr7drG8ZkrYOvBbNrEfaufbplHPNpu9zzzocx5pC\n1lyBAIm0i00UBfG5Ev62Gf63kE7Lm3JSJUVj5CXihvLZ9zWhueT69s7HWvR8P127110qpubvnsWL\nBZ4HLZeD0+wxmfpsmR0rPfqBwZpY/56ztHhEQgNHx+e71EfsxA2WpTHM8gst7pK2dZka18sePt6G\nrGiIgdGnNQ4uj+caDrCwjnzbLc3f+biQUuyERtY7lpeDhDX2qCe/arTTT/gmlzfZk9o/GL8b0LqX\ngHyPh6xzJT/3QpPWNHjjI5drSGMnlOtOZOpD4wPckPvfY9QReZ1Awps/5TZ4PVVCgzwcHXNmd+j7\naRwZenMf7jt4XOC2Zt9vXqOQ0Jv71x7X9zxrrE6Ilr2cjL0RJkW/IAshhBBCCOGhCbIQQgghhBAe\n0yWxyPNroPhz5taBvI1yxk/ubK3i/3oflei3fSqXscUTnZytj/g1hX+fuS1sAn6Vm+PXJfw6heUg\nCTmH3X/4bUlZ/Eoo8cop49UMW/j5r4EScgx+fUWvr9lyh9ua7QWjhI2UB9vVkcQoIbmgJz/Vuul+\njCGuT3qQEq+CuT+h7w9m8Q85T4owrGHl5xsYGCH1CdU6vufsdjEwqhX8fuD1V+USBkKHvpvYgpzS\nuSKWdch9YVrc3K33oHeTDPtDkACwHIDGkaCSMa7QONE9M/rajs4dscyPx1LKH1JfFNZZa+Z/ebx2\nSoxJma/Wx9ie/JiQNe5y/4F5lM6QdyVjEtO5np/H/Vj6tfg+eJxIjBtQkIxy8m1l/FybZt96t7oS\n/YIshBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeRapDH2d42c/tg3oaQj8/QBIH+mbRgrkc6YdpC\nkTXGiaKl7L6akOWlnimp+UlYvKTYd2XpdI6tXjlFt5bQBfN3M7ZcTms7M9R8c1sk7JBS9Mtm2THu\nb12dKGdG22bFScIGLk1bdq+Qun15+lb2rDFO/NTAayLS7KsoL9wl+78qNkYxj+mgQNr2HOlTvcYP\nyItsOOTvkj0Yl5s1x1xPvL0sfPmYaI5TdNQJ2zE/yTHAt0uWeYUytmNhBtPcjqHXdhHV5aBDWvJ+\nRjtlaM19O7ssS9WEtpSyMwe1tPxjEDJ7Is33LctCMmNr76y+HNZyZQjCs/r9hAaZ8tPGsMT6qaz5\nH+uf9zG3mRT9giyEEEIIIYSHJshCCCGEEEJ4aIIshBBCCCGEx5FqkBOa1wPUwEYZ2lHHWyr2U0Qs\nGdqYLA/UhP7T18iOvuqe8sfh2GqMs0jVkmZ8d/SuvO+lM3wg/W+w/irM+O6Qnj7ejjhH23pCXtZ9\niWzG2VKYydKb8vfZ8zZlW2uj7ZtZ97vbxL2oua8bks449L4fBnk6ljTGrIfM0hgfB13xOESW3p9w\n/aS1Y8AdP2X3aCt59t/n/sOLoYRfdZY2nPu5TC3qPtp13HHmXgmhcZ4F71ieHyQOzchn9jPOJ7aO\n5qlNmhc/fyFjv+3M+5qCvkW/IAshhBBCCOGhCbIQQgghhBAemiALIYQQQgjh4aKEmOkuXsy5m2Z2\nycyWzGzt0C68d6a1XGZHU7aHoihaPuRrJlDc7Iv7PW5apraZhMMu2zTFzDT3NWbTW7b7va9R3EzG\n1MbNoU6Qb1/UuReiKHr20C+cwbSWy2y6y3ZYTGsdTGu5zKa7bIfBNN+/yja9TPP9T2vZprVch8k0\n18G0lm1ay2UmiYUQQgghhBCAJshCCCGEEEJ4HNUE+fNHdN0sprVcZtNdtsNiWutgWstlNt1lOwym\n+f5Vtullmu9/Wss2reU6TKa5Dqa1bNNarqPRIAshhBBCCDGtSGIhhBBCCCGEhybIQgghhBBCeBzq\nBNk59wnn3GvOuQvOuc8d5rXvUJYvOOduOOe+5/1t0Tn3VefcG7f+v3AE5TrnnPtT59wrzrmXnXM/\nPy1lOyoUN3sql+KGUNzsqVyKG0Jxs6dyKW6IaYmbaY2ZW+U4VnFzaBNk51zezP43M/uPzOxpM/sZ\n59zTh3X9O/CcmX2C/vY5M3s+iqLHzez5W+nDJjCzX4qi6Gkz+5iZ/eNb9TQNZTt0FDd7RnHjobjZ\nM4obD8XNnlHceExZ3Dxn0xkzZsctbqIoOpT/zOyHzewrXvpXzOxXDuv6I8p03sy+56VfM7PTtz6f\nNrPXjrJ8t8rxRTP7+DSWTXGjuJnW/xQ3ihvFjeLmfo2b4xAzxyFuDlNiccbM3vXSl2/9bZpYiaLo\n2q3Pq2a2cpSFcc6dN7MPm9lf2ZSV7RBR3IyJ4sbMFDdjo7gxM8XN2ChuzGz642bq2uU4xI0W6Y0g\neu+fMkfmgeeca5jZ75nZL0RRtOPnHXXZxGiOum0UN8eTo24bxc3x5KjbRnFz/JiGdjkucXOYE+Qr\nZnbOS5+99bdp4rpz7rSZ2a3/3ziKQjjnivZe8Px2FEW/P01lOwIUN3tEcQMobvaI4gZQ3OwRxQ0w\n7XEzNe1ynOLmMCfI3zSzx51zDzvnSmb202b2pUO8/l74kpl96tbnT9l7+phDxTnnzOw3zezVKIp+\nY5rKdkQobvaA4iaB4mYPKG4SKG72gOImwbTHzVS0y7GLm0MWZP+kmb1uZm+a2T8/SvG1mf2OmV0z\ns4G9pxf6tJmdsPdWUL5hZn9sZotHUK6/ae+9Xviumb1067+fnIayHWFbKW4UN4obxY3iRnEztf9N\nS9xMa8wcx7jRVtNCCCGEEEJ4aJGeEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHvuaIDvnPuGce805d8E597mDKpS4t1HciElQ3IhJ\nUNyISVDcCBdF0WRfdC5vZq+b2cfN7LKZfdPMfiaKoldGfadQqUelmcWJrmdusq8dCpNV4Z3h++Rz\nZ+Uf4Lk7Ny+vRVG0PMYVsoswQdzkZ+pRYWlh9EnT7mPc+rub9X832e/zsZ84IvpvX5mOuGnUo8Ji\nSn+T1paOKiSig3OUPxwz3z8/n/soYywr3u8SwcaGhc3WgV9t3LgpzNai0sq895e9VwgPpc6Nl3+H\nM2YdcGBEFINpZRv/PiPKH32trO8ynQurB97XvHfd8eImP1uPisvzd8oys+R9jUWif0iPyWR97+Pa\nGWTH8HTSe+vqnuKmsI9r/JCZXYii6C0zM+fc75rZJ81s5IBVmlm0J//BfzPRxaKM37qjHAXJkIIk\nj/m5APOHXn4uHDOi+KHmr6fF85AO5RYZc4KWOq6POfl76X//pUt28IwdN4WlBTv13/2T0WfczwSZ\n6j/xTiUrP0zpIfi7eSoMf5fzeVLl5/N9Zb0LyprwHeAE+Z1/9LnpiJvFRTv9y78w+oyJ+o4/RkXM\ncwHedFTCxnX9XHp+j/Pj87s+tzOVk66dNfnO7H/SyIqruzSBvvo//y8Hc6IkY8VNaWXeHv+NT99O\nZ03ufIaUl6PvhpSfz5j8pU0OsyaOyUlo+vGDEIMurWxZ98H5hRw+C8EQg8r/PtdhPsedKPKdv/8v\n70ZfYzZm3BSX5+3sr/3XI0/G9wVTABoHHPVLwwHVVxHrJAwwv1AMMT+M83PUd+z3cU5eKz5Dnu+D\nxzO+duY/pMY7H54Mj33zp391T3GzH4nFGTN710tfvvU3wDn3GefcC865F4Juax+XE/cIY8dNuKu4\nERPETVNxI7LjBsaobcWMMLMx4ybcUdzci9z1RXpRFH0+iqJnoyh6tlCp3+3LiXsEP27yM4obsTcg\nbhqKG5ENjFFzihmxN6CvmVXc3IvsR2JxxczOeemzt/62ZzLe+qTKAyISv0T0WpIlFfz9If/Bjc5j\nyUWuj1/NBZgutvEVyLBIZfX+WRKU6Vo9PFdY4jS9gupj2RLnG6S8pjsa/ey+4ybzlXE04vOd0ixz\n4Dd6Wa9x/OM5pPjc1LYJ+NV6ODo/KnBBMyqFJEiJ1/YsN4DvZsgzDofJ4sYPcu5DCnxf3meSSHB/\n4ioh5ZMko5Dxat6r06hoqTj+GYOrn1+bsuTC/8wxxWQ19fHTHO6rv+F241flaXmJrofys5qiQB10\n3mvnLOkBlyUI038L47JVyvEgx1KRE9U2npskE0uVJqTXuo3Ua290arc/D6icw+GRGW2NFTdRlC6/\niWgs8A8dkkTCkWg46uPAEPCYRGmajqRLdShdrgzw2mPr5uN74esGg/TpJktJcsX0GM8XvCcoU6c9\nGfuJvm+a2ePOuYedcyUz+2kz+9KBlErcyyhuxCQobsQkKG7EJChuxOS/IEdRFDjnfs7MvmLv/bb1\nhSiKXj6wkol7EsWNmATFjZgExY2YBMWNMNufxMKiKPqymX35gMoi7hMUN2ISFDdiEhQ3YhIUN2Jf\nE+T9kpCNkCjL1945lMYkNH+sG2ZNclClc5O8xdf6FmlBKmv6WF2Wo3IP6mS70iV9onftIumbiy0s\nWPtkPjU/qGDZym0838BbO8D3fGwZx66O87Ks1liX2qN81kX53ydrMOtg20Vl/G6uRflUtnwH4yis\nxt934WjbMDNLaqkztGOONbdp9nXHCJemIWeBmafLTmiKu3Qw27hRW9ssdlhuQP2TpzvOkQXckM6d\n6qFsd7CgY/2z7zPLOveEUDbdMo7rJc3/mTX4d0kmeMBEqZpNtjTzdaes1c3SJHNkhqS3TWqUveef\nxKClPNltZVjO9QMMhJD6k91O5fbn03M7kNcJUDT/d1fQ+ez7zdOQ/qmVv4b0n64/CWnzxua1Ni52\ny9GgNZzKmHlPm+tSdOGOHjyIMRpT2IotoOcoX6a2btEiBnomh914mhdV0pXvrS5OCSszuHAmn8ey\ncmyst2I9eamA12rSfbU2cFJWrGOfOdguQ7owi4u/Bu140lasYR5r1zn+94q2mhZCCCGEEMJDE2Qh\nhBBCCCE8NEEWQgghhBDC40g1yCwLSWhk/a1faSqfJ09Z9h3lc+VIw8y64dDzD66s45fz5CU8qOZS\n87vz5EtIOuFS0/Oz7OF3K2tdSLsQdThcZ4MGaW0GXA9x/qCe7g19bEnzOs7wjExokCmdoy2EjbcM\n9g7P7eKhrH8t7rCenI4nA0uO4d4CC0hjggbp1mqkKa5STNfwYmnbfLI/JXtjHhcSu2uzntZP87Hl\nFO25WVJ/3qO2Svn+kPISmmLWDbK2l6/N+Ien+V3fCdJecz04ig3/2Tsau+yDhZ8LthN33kMa0vNe\n4K6F8pPe2Ky3peO9vpz1ylkaS76PPOlBq2XUcO62Yg1ytUCDJ/F0Be2BHyndhPSA9LcfmL0K6T+/\n+djtzwuVDuRtdlGnmpvmQctrL27baMiH7v0+2A+4QNpeoz1KymVsr6Aa1//iLA46WR7XORpcb3bw\nYitVHPTK+Xhc4bYL2O+Z1m0ELZyO5insgiFtCuHN+cIBnis3bj83Av2CLIQQQgghhIcmyEIIIYQQ\nQngcrsTCoVQiYXuTeAUaf+btnYfkbMISCpYuFFC5YIUOvZL2ZBCNd/Hg3hL+tF/o4iuP7Ufw5/3S\nDp67t4A31rgavyIpbeGNRXn8N0vtjTVIdx49gWXpsO0bWUZ57/kSEha2cDpSwc3ksLwGYOsdlliU\nSVpAr4GGVXqdRa/OCpX4lVKwTa+A6DXmsECvlFjyQnBM+8/DkNoqmMVy5slmrFanB4DodvGBCvue\nNRDXWZZf1bQQoXVkopgBy5M82QPFVI5lDyz5ourl/BzbvHmvAElFlbC7DEmileuThGuevsAWdF4c\nltgOiey9HMVsP6xgPlvQkQTJv69knU3FluVjwa/K82yn5kkd6iWyqSIZRKOMsqZ+iP1BP0jvgLve\nVr38CG7u1iDNr9nZ9qpKZfUlFWZmoWcD98qVU5C3vICv1dcD3Ep6SL+7LeZx6+l3OouQnivFsoqN\nLukFiDQLvqMmlx9t85ajrep9mQTHWJElFAS3HUtxerSlc7cTj0tzZeyoWGJxtrYF6U6I48LjMzcg\nPVdAScz3m3GssMSit40xxrK0XIP6pi0cT10Dn5+o7d0nKynZGi9tjpCCfkEWQgghhBDCQxNkIYQQ\nQgghPDRBFkIIIYQQwuNwVacR6mATcqLEtqZ3/myG2lqzpBaXNSnlTdT1DMukUfHkL80HUSvDNm2s\nGQxQ/pXQG85fwGvXL0ocFfoAACAASURBVG7H353Fa+UGpD/a3IZkZ3kF0qxX7M+QXtGT7bBuNav+\njy3ejSU0a2wHSLrIHOkESyWs4Lkaaq58W6LCmfS9vB9toP3RX1x/BNL1EmqwLq/PQ9rXrQW0Vexi\nDbVlAekbuz3SGJP2dEh6XF+rzXWU3Hp9SnEU8xwLfMu+Jo5tmqgOCi3akpktvtAtKfHs+d/Pk3Q9\nqJP1400saHeZNMZkPVhaxBj113awnVeW7rVPFlNRwPdtlB4dG6nbfk8RqVtN05oGP816zjLplUvk\nLepbYpmZRUVsm50+bbXrPbPtLq2LId0qa477/Tzl05h2FQcxfwwLK3hf63Sf/34bt47+xyf/BNKL\n5Nf16KmvQvovOw/f/vxHa++na6EmudmjwXeKYD2/D+uMB179s3a5QHFTpPxBynXMzDodjI1GIx4b\nbjRRL86xvtHBONjtYH0/uYwaZObS9kJ8rhuzmJmxBmrY4U6SxupdWnhWiE/IVqTFCnmmToh+QRZC\nCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC49B9kMGXl/asHRZJF+VJadgTNql9w3RpF/9Q2qHtF+t4\n65uPxRdrPZguxk1Id0mnV97Af3eUt1APE8zFuuPtR1Hzw3rm2aVHIR2W0j1pK5tYlr63FXVE+6S6\n4N4QHfPWx2m7eOZyrEHG+ioWUf/1yIl1TDfQl/on575z+/MHSpuQdzKPjbkzRJ3wF+uXIf336hch\n/coA9Xcf9bxNv0O+4H+w9RFIv7R1FtJv3liyVBLb4Mafx9gVdbqIyDOYfg5wtFbAl+Plu+RNilau\nCV/2ykb6Ggjuv/y1Ar1FWjeQUi4zs6hEMTyPhSmVsL8peprGFmkKwyBdm5rbpSGCnzXSJA+9svHW\n6exdn/BlnwKiyIF+n3XFXfKY9Y+dq6Z7zC5USJhOtAPUjobkI9v32orXENSrPUiztnSpgVsM92iN\nwmoN+yrXjM9f2sJr9coYQ3/yxhOQZl/eJ+rXIX26iP1k3guEE2UsZ5d8eDsDCqIpIl9I8UGmccbv\nU8tF7BxWGtjZnG9sQHowxLYr5/BBu9FDnfFqK9YCh9Q21147CekT38H8pRs4Hu40z0G6dRpjdvsH\n4+9XaJ1G9xz2U41FbOs6rY/o9LGty0W8z/XN+D6L1Oel6cHHQb8gCyGEEEII4aEJshBCCCGEEB6a\nIAshhBBCCOFxuBpkM3PDWBuV0MSmaNLYB5m1uuVtPFdQxbl/tIxamdwANVrFdpxuvI3n5nLNXEG9\nS6GNOh3W5RQ3UJu2/dTM7c/tU+y3itdq9rGJFl5Dj9NcF/VLrYfI59Arey7M0BxPq9Y0MtQ+8m2Q\n56evn2XP3iHpxdnvdlgjvTidu0jG0yfysY7qdAHrntkakj48wrZ9sYe+x0+TpjmM4rh6pYca42u9\nOUhf2cY0axYTPr99zHeluKys8U6KYqc0cJxZ5Hsbc9OXRz8PfEusny02SV9HvzVU10hzSJfy1xJ0\nT2BmWKXvhlQYThOtJnqrD5ujdZusIeZ0YZfihEaMwQI+D87TfEf50Z7s7+WPLNaR4VyU8Dr2oSEL\nPGtZH1slben19gzmFzC/Sb7HIfVdJc/ruEe+5rMV1CDPlnHM+fD8u5Be7aFH7dMLqBO+0o77j7fX\nFiEv18MgiLZxvPuT7z4F6ZdOPwDpHz51CdIFzx96toDlvkT9bzic3t/0wpTnMiTNt6/9d+R7vrqL\ncVIhH2nWZbO/9moLv9/sxnHV3sUYy3doz4Y3sf7dgHy/mxRnXXyoW6fjMbD5MG3SQF7FC7SnAD8P\n8xXM3yJNvu/vzD7/yU0eJmN6o00IIYQQQogjQBNkIYQQQgghPDRBFkIIIYQQwuPQNcigO6bpeYhS\nJvOlNUMqKUtMmmdQS1O9SceTBmjubdTSLL4c61ncALUzQQMLFhWw4EEdzx2UsSxrP4h6r/6ctw/7\nj6DOdPcSakejPF4rF6IOJ6hiOo+3BVrrwGVoAg89GvaIM9RHs9SLJYP+Hu59Opj/SUgyqSFpMC+T\nlpc9KDte0P6L9gLksX754toJvHSA+fOz6JP6t069aaO42MJzXdrGa+9ukUifHpiI971nf+i8V6nh\naI33VBMlvY59CihxA//hPOWVN/GmS00MukEtXRfcIa/j3okRB5pZcRfrm0LOGpfxXINrpM27TLpB\nz16Uz9WbJw1+AdN97LosmKE44efLz6Nnaxo1x0wUucRz69OnZ9b3jS4W8IbbpIssUf5ODvWgPdIw\n87qBXifOz5Hn7lIVvXMfqG5Deo4C+om5VUvj42feuf35v5/5Mch74SZ64W69hV66gzmMic23UMP8\n/+6gv/vy4s7tzwGN06wHH05x35PPjy5cPk/9hVdF7D3+wMIOpM/WtiDdo8nQt2/iepStXewP7K24\nvusb6XOA7fO4fqH5IPU1dRxXSrT2q7virV0p0x4DM+hz/NHltyHNfs55Wvz18s5pSF/ciuOqVsG6\n75FfeWINzh7RL8hCCCGEEEJ4aIIshBBCCCGEhybIQgghhBBCeBy66tT34mUtI/t9Dj2J1gBlS9Zf\nIoFbHfUr7Udw7l9Yx1vNhaj/mvFsIktXUAtqpEG+8RHU6RRaeCOtM/h19kzt/UB8/n/0yIt47nPo\nYXi1gxrYb373UUhXbuB9VVh7PbzzZzOzYWn0sVMF+yBn4euIWSLJmlT2A25hfTYj1Fy1drHt33w3\n1t/l1klzuIkxOFggPd0SarK2m6gde3XnFH7fE5C+u46eyQPyJrUdTLO/bVTjYKD8Ybqm9ljgzKLC\n6MDxvYjNzIbgmYx57Ffe75I2jyywc33WHJO3sdddRedQH9rfYR911GU68jOfv4Dnnn0F1zXY5Vhv\n6moYY92nUb+4/gz2iwz7Q/N9+j7ux0FzzDgXWSHne4CTL3QBxxnflzdHOv8haZk7PdIvky5y0CYv\n18Ho36+GVO/Ds5h+dRv7jreaS5CeLaLf7a+e+TeQ7nqD8wcb6KH8jdWHIM3PWP2d9N/d+tvYh65u\nx/ddWcZnoVQkXWqKR/VRAz7IFDfskTxoxs94WMW5zCtvo2/0Ww1csNCnvRGiVazP2tWUuKEQa30Q\n42BuBfuOf/rwn0D6W62HIf2Hb74f0iXvvvmezy2hlrpFczDWVn93AydSG23su3xdMT+nrOuedN2M\nfkEWQgghhBDCI3OC7Jz7gnPuhnPue97fFp1zX3XOvXHr/wtp5xD3H4obMQmKGzEJihsxCYobkcZe\nfkF+zsw+QX/7nJk9H0XR42b2/K20ED7PmeJGjM9zprgR4/OcKW7E+DxnihsxgkwNchRFf+6cO09/\n/qSZ/eitz79lZn9mZp/NvBppSdknM99DoUjO07Cwf2e+iXP70inU0szXUct0vYZa3l3yCpx7Iy5M\n8ynU/NQvoi9hUCOPWdK/9E+gbqrwdAvS//mj3739+Z8tvQZ5zSHexxdJ0Nx9BkVEf11ET8r+HFZU\naSuupwJJq4vNu2cqeaBx48yc7/vJQsgi6tJynh+lI11guYL7vRfzGIS7pANm7VLhIuq9yltxWao3\n8OBBneuX/LPPYJw8sIjepcxuL9ZsDViHxl7FM3hu1hQ78uyMWKPsH8965LsoTz7o/iZNV895eU/X\nyfpZ9kwOMAxsMJuujWQNuG/52d8mzfEWXjyinzE6K9RWeTy+dg37J/dK3H8V5tDYuLiLOvhCB8vS\nnxtdbrOk9i+oxnGV75AusMSLTuzAOKi4iSJnA8+Ll3XF/X6RvxIfS/pY7jv6Xfwur3cobpLWnELK\nX6PTO4X92PeuoG41WMMArV7Fc/fnsHCf+gBqlBer8WBx8SaOh8NLuCBo/gKWs7qJQdKlMSmkfQLy\nnTjA+7SWgrWk5TLe9345yP4GfZDTx9bSYtyhtHdRi1ubxTlA+woucOC1LXNv4Lkj8rS/+bF4jPsn\nf+uPIe+TM9+F9KNFvNbzHWy7Kx1c+8Lj62Ijjpv5CunJqfNY7eB6q3d38If6jS2Ms3oD68XX+B+U\n5piZVIO8EkXRtVufV81sZdSBzrnPOOdecM69EHRbow4T9wcTxU24q7i5z5ksblqKm/ucPcUNxMxO\n+06HiPuLCeJGfc29yL4X6UVRlOoxEEXR56MoejaKomcLlfqow8R9xjhxk59R3Ij3GCtu6oob8R5p\ncQMxM1u70yHiPmXvcaO+5l5kUpu3686501EUXXPOnTazG5OchCUW/EqpuBP/odDGn9DZOq39IN7K\nTg5fMYW79GosYVcU//xff5P2a6atpcsbmM2vQPmfHR95AG1yfmLm5duff3sXX19d7C1D+v++9EFI\nb26Sn1SQ/vo717/zZzOziLeevvv7eE4cN2m2Y5zjv/Yp0Ss5fiXE28Pya9Lg5mhJhRnGLL+O3nkU\nz1V4AH9lePYsxsV/vPQSpL+88QFIN/vxq7hSGV9XDXL4KozvY0hbuHI9hDmSaIR3UUcxPpPFjUOp\nBL965Oc29OQBPCT28c2iuRXsIwpkRzW4igNmHt8OWsmLIxeSJR/3ix9EidfSDMbR6gbKJoIqvQJf\n+aiNYvNx2jr9FMVNGQvj+DmkOIk8uRPbmCUe1Lu/bfDYceOcgc1bSBKSpIzC3fGzWfK1b0KqVKJz\n0TPMllzhibgDP3Ua7bhWr+Lr6TzZwJW3qN+j8fPmPH5/ZzHu9/o7KAGo0HdD2ua3tUIxdXK0DMfM\nLJyNY4weUSsWMf4Kh2PzNlF/k2bzxgSDuI4i2r683cT6Lu5gPkuXerSEsLuElfhTz37n9ue3Oji/\n+NXtvw/pb3zncUivfJ2sCk+QfS494tc+Eku0IlTt2HIV+603N3Du030DZbBRHdu6mTYHoMDJUZrH\nu70y6S/IXzKzT936/Ckz++KE5xH3F4obMQmKGzEJihsxCYobYWZ7s3n7HTP7hpk96Zy77Jz7tJn9\nupl93Dn3hpn9xK20ELdR3IhJUNyISVDciElQ3Ig09uJi8TMjsn78gMsi7iEUN2ISFDdiEhQ3YhIU\nNyKNQ99q2oc1VqxB9l1BZt7ppx7bXULdnSPHrPI8abB26fsn48UZtRcv4blKaH1U2kGtTEhbNhuV\n7WuvPwbpv7gQbxedI7utShXvk7eVrDVQ+9giq6CI7bt8ORjLlUlznCGbOlJAY0QFZf1RwdOtnWjg\nqnS2nmkNsPHWQ9J4s4yQYtbXde88hg1fewi1ox84eQ3S52qoI/zazhOQ/vYqbgPc3Iot6NimjeMo\nDEjPSHHC745cn/7ga08T+wvfPbuuAyVCPS9vZc/Wa75MbchbVPOW5JTda2EcJTRxVL99rz+iHVct\nR05Wp0hz/KETlyH9YoRWj6sfIk3jmfgCUZGeeepv8qw5Zvu/Pvnf8dbt/vlpzcNxIIrMhikdYUDP\nFX8X0rTVdK7Ii2zo3Ccpu4YV+HfOv3X78zONq5D3ldLTkH79bdqmfgbjs7ZKGk3SLPs2koU6BmRv\nmbbInqUY4VnFEgrwq3Rfdc9qs5DHOgqP0Zb3vs1bRMHAmtiyt2ahabjOpUC6694sBkp4HutvWML1\nD59+6uuQvuKJlP/oAsbJwh/iotSn/wLHqOAizoUWn0KN8rCGcXW5FM+NrtL6hpsNHFsHpG0v0OMR\n1chTkvpv/3mi5VSJ5Q2TRpG2mhZCCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC43A1yM5ADJKwpmON\nbBgfMCzjXD4sYbp6E7+b75LmZwvzG1dQx9NeiXU+hadR+9k6jTqbtR/Ecw/naGvpNdQMFa5jNftb\nPrPvaKuCupyVR9YgfWMNPU9zXayH2lXyTPQky4U2lZtbf4rlXrCVMmuVaB/ynucruVVEfVef/IAH\n5NU6HHB94vHlTfL89HcCX0J9+MceQP3Wf3ri/4P0/7mG/rRv7qBxZHMVNVugEyZNW0ieqvkdijn2\n1mW9JP1TOfA1zXk+GJPTHDdAxpbZQ+8+E+sjFlFHmfDZJO0uVbf1F7G/mZuNO4HHFvEZf4e2XF2q\nNiF9vrIO6d15jPFegG2/7mn3Itaak65vuIl9XVShO2EbWv6Jxb8Wxw1vd373fZDHxjncXpr1yCXS\ne/qwRy97Js9XMYZmithfnCij1vwDM6g1f7p85fbnMwVc3/D1zUcgXZ3DawVVbNed87SvwAquzfC3\ndC5T57FBMRN2MN5ypB0tUp2VCpiuluJrdQe0pobXm0xj0NzC90Hm/iHhie2Rp/otkad6t4751RrG\nzZk5XHD1/I33Qfr1l+P5zMxbOJ7le3junQ+hdr0xU4V00MD5ybCM5yt6Iex6dC3a3jysUpw8hjE4\nW8Y+szfATjnvPV/d3ugt4M3S91FIQ78gCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOFxtD7IedSF\n5Hvs4evp2XJ47NZjtN/7CvknzqK25uwfpf9boHkmPv/aD6Je6/z70XPylx/4FqS/3zkN6YA0sV/+\nxocg3Xs41t7Mfgs1PaVdrIOt6yuQzj+NesSAtI3dHp5v9mL8ORpt4fle/nHRknJTkq7SNWJNG/tI\n18vp5qzld7D+2JO2u0iaOC/sfuKJ70NeK8BzrQbonz0kEey1TdSXF3ZYL+YlSDQcVkiTf4O0pekS\nLQtqpA/ztaSsO+VzsQHsFAExzR7h7J3pefhG5IPMPtPBLlaCI+16bh7j7MMPvQvpf3HuD29//svO\nw5D3SvUBSH9z7SFIf+U6eple3sK4Yv0j5DXxeShtYLlL5B8fVkmz32AdIWktK16a+xPWj06lx20E\nOlfWwLJ+ttOLx4qlBmqIQ/JBfmwWF8p8sIEa4w9VcM3Cj9Az/XI/1mh+cQfHFNatd6/RvgBz+BBz\nfD+xjLr2+XJ8racaq5D3rZkHIb3RQS9d1sDnSYvN6U4/fpZYY8wacNZ5TxP+czektS2sw25347hh\nfWyPxqyZRYyrRxY2IL3VQ53wpTfRUHv+1fgZzvexftsr+Hw3z1Ef+TfnIZ07g/sKDDawrM7TRz/4\nAMZUpYCDaa2AfeR2H+9jQOuFun3sc/3YSMRNYr3JZGOUfkEWQgghhBDCQxNkIYQQQgghPDRBFkII\nIYQQwuNINci5kHQhbNHpSYH7ddSjBCh7stwZ9NAb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/79cp+SGvMoYjK60GBlz+BAq9GP2r90pdvXkgJpakaWcG6Ne1A2UqrHkGdVHEc7b5e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dNlsekr2VK6AIpXSBL60KbdU+kW82P1lHxuXNd0gleQX/K0jaeu7pOcTPD\n+s/4c1Bhf1C8dsJPlOtsDE3W3dYc3zWcoa6YtZApAjOX4UkbLpDmk/21K5g/pCDt9Ec/l8Z9V5t0\nfuz5Sf6igzppmD1tK3su5wLqV0mTzNr1QQ2vndBqj+O7frj+2nvHbw/ycnWD0YUukEd9jrTg7M1f\nu44x1lnH/sENMd0exuLT3iKeaweTlud2oZgI6yzapKQXcoVtWidTSFdmspY0ax1IWPYuzlLqScWj\nR8E4ZfWPjXhcTu/bcygLTly2tJ24mHduzAlobWEnj33NgNbNBOwdT0TtuLClFq974XkQfneIS78S\ncZS82OisgwobbTUthBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeha5DTtCGOfe58ORvvW89aMNYA\nkk5nQFqb5jnyNS16eq+HH4S8kLQx7VPpnrIhSscSZevPxzcTzaI5Zp48T1nxFpImaEj+rEberr6e\nKVH3d9kH+SBJjZsxNW9wLH+X9F5BdXRMmqFWr0ibKRVJg1XZwIs1LmOQDhr4OOYphjsLceG4XKzf\n4rZN6IhZo5gwbbXjT2RmYcqNsN7Rq4SsR4F1v6wbDlvsBUvlKMWxkOuRxpN0r3nSsrM395D6TW7L\n7krcp/he2mZmYQmP5ZhL+K5z3JBnc5Sm3U7xiZ0qQB+6968lnkH6clgiD2nymC7t4PGFLq1JaMTf\nr97EK3F/MKD9TriPLG3QOEIzAX985XhMrF3hviZDSzosjD5fNMbeCFMHLEhJvw//0Ii8hfn55TEp\nrPLJMMm6Yn/NE/v8Z40brIe2JgUK5Rc8n2XWHGetQeA5W7Iwo+spUd2JAW0y9AuyEEIIIYQQHpog\nCyGEEEII4aEJshBCCCGEEB5H64NMJPw9fe1Sxr7drG8ZkrYOvBbNrEfaufbplHPNpu9zzzocx5pC\n1lyBAIm0i00UBfG5Ev62Gf63kE7Lm3JSJUVj5CXihvLZ9zWhueT69s7HWvR8P127110qpubvnsWL\nBZ4HLZeD0+wxmfpsmR0rPfqBwZpY/56ztHhEQgNHx+e71EfsxA2WpTHM8gst7pK2dZka18sePt6G\nrGiIgdGnNQ4uj+caDrCwjnzbLc3f+biQUuyERtY7lpeDhDX2qCe/arTTT/gmlzfZk9o/GL8b0LqX\ngHyPh6xzJT/3QpPWNHjjI5drSGMnlOtOZOpD4wPckPvfY9QReZ1Awps/5TZ4PVVCgzwcHXNmd+j7\naRwZenMf7jt4XOC2Zt9vXqOQ0Jv71x7X9zxrrE6Ilr2cjL0RJkW/IAshhBBCCOGhCbIQQgghhBAe\n0yWxyPNroPhz5taBvI1yxk/ubK3i/3oflei3fSqXscUTnZytj/g1hX+fuS1sAn6Vm+PXJfw6heUg\nCTmH3X/4bUlZ/Eoo8cop49UMW/j5r4EScgx+fUWvr9lyh9ua7QWjhI2UB9vVkcQoIbmgJz/Vuul+\njCGuT3qQEq+CuT+h7w9m8Q85T4owrGHl5xsYGCH1CdU6vufsdjEwqhX8fuD1V+USBkKHvpvYgpzS\nuSKWdch9YVrc3K33oHeTDPtDkACwHIDGkaCSMa7QONE9M/rajs4dscyPx1LKH1JfFNZZa+Z/ebx2\nSoxJma/Wx9ie/JiQNe5y/4F5lM6QdyVjEtO5np/H/Vj6tfg+eJxIjBtQkIxy8m1l/FybZt96t7oS\n/YIshBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeRapDH2d42c/tg3oaQj8/QBIH+mbRgrkc6YdpC\nkTXGiaKl7L6akOWlnimp+UlYvKTYd2XpdI6tXjlFt5bQBfN3M7ZcTms7M9R8c1sk7JBS9Mtm2THu\nb12dKGdG22bFScIGLk1bdq+Qun15+lb2rDFO/NTAayLS7KsoL9wl+78qNkYxj+mgQNr2HOlTvcYP\nyItsOOTvkj0Yl5s1x1xPvL0sfPmYaI5TdNQJ2zE/yTHAt0uWeYUytmNhBtPcjqHXdhHV5aBDWvJ+\nRjtlaM19O7ssS9WEtpSyMwe1tPxjEDJ7Is33LctCMmNr76y+HNZyZQjCs/r9hAaZ8tPGsMT6qaz5\nH+uf9zG3mRT9giyEEEIIIYSHJshCCCGEEEJ4aIIshBBCCCGEx5FqkBOa1wPUwEYZ2lHHWyr2U0Qs\nGdqYLA/UhP7T18iOvuqe8sfh2GqMs0jVkmZ8d/SuvO+lM3wg/W+w/irM+O6Qnj7ejjhH23pCXtZ9\niWzG2VKYydKb8vfZ8zZlW2uj7ZtZ97vbxL2oua8bks449L4fBnk6ljTGrIfM0hgfB13xOESW3p9w\n/aS1Y8AdP2X3aCt59t/n/sOLoYRfdZY2nPu5TC3qPtp13HHmXgmhcZ4F71ieHyQOzchn9jPOJ7aO\n5qlNmhc/fyFjv+3M+5qCvkW/IAshhBBCCOGhCbIQQgghhBAemiALIYQQQgjh4aKEmOkuXsy5m2Z2\nycyWzGzt0C68d6a1XGZHU7aHoihaPuRrJlDc7Iv7PW5apraZhMMu2zTFzDT3NWbTW7b7va9R3EzG\n1MbNoU6Qb1/UuReiKHr20C+cwbSWy2y6y3ZYTGsdTGu5zKa7bIfBNN+/yja9TPP9T2vZprVch8k0\n18G0lm1ay2UmiYUQQgghhBCAJshCCCGEEEJ4HNUE+fNHdN0sprVcZtNdtsNiWutgWstlNt1lOwym\n+f5Vtullmu9/Wss2reU6TKa5Dqa1bNNarqPRIAshhBBCCDGtSGIhhBBCCCGEhybIQgghhBBCeBzq\nBNk59wnn3GvOuQvOuc8d5rXvUJYvOOduOOe+5/1t0Tn3VefcG7f+v3AE5TrnnPtT59wrzrmXnXM/\nPy1lOyoUN3sql+KGUNzsqVyKG0Jxs6dyKW6IaYmbaY2ZW+U4VnFzaBNk51zezP43M/uPzOxpM/sZ\n59zTh3X9O/CcmX2C/vY5M3s+iqLHzez5W+nDJjCzX4qi6Gkz+5iZ/eNb9TQNZTt0FDd7RnHjobjZ\nM4obD8XNnlHceExZ3Dxn0xkzZsctbqIoOpT/zOyHzewrXvpXzOxXDuv6I8p03sy+56VfM7PTtz6f\nNrPXjrJ8t8rxRTP7+DSWTXGjuJnW/xQ3ihvFjeLmfo2b4xAzxyFuDlNiccbM3vXSl2/9bZpYiaLo\n2q3Pq2a2cpSFcc6dN7MPm9lf2ZSV7RBR3IyJ4sbMFDdjo7gxM8XN2ChuzGz642bq2uU4xI0W6Y0g\neu+fMkfmgeeca5jZ75nZL0RRtOPnHXXZxGiOum0UN8eTo24bxc3x5KjbRnFz/JiGdjkucXOYE+Qr\nZnbOS5+99bdp4rpz7rSZ2a3/3ziKQjjnivZe8Px2FEW/P01lOwIUN3tEcQMobvaI4gZQ3OwRxQ0w\n7XEzNe1ynOLmMCfI3zSzx51zDzvnSmb202b2pUO8/l74kpl96tbnT9l7+phDxTnnzOw3zezVKIp+\nY5rKdkQobvaA4iaB4mYPKG4SKG72gOImwbTHzVS0y7GLm0MWZP+kmb1uZm+a2T8/SvG1mf2OmV0z\ns4G9pxf6tJmdsPdWUL5hZn9sZotHUK6/ae+9Xviumb1067+fnIayHWFbKW4UN4obxY3iRnEztf9N\nS9xMa8wcx7jRVtNCCCGEEEJ4aJGeEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHvuaIDvnPuGce805d8E597mDKpS4t1HciElQ3IhJ\nUNyISVDcCBdF0WRfdC5vZq+b2cfN7LKZfdPMfiaKoldGfadQqUelmcWJrmdusq8dCpNV4Z3h++Rz\nZ+Uf4Lk7Ny+vRVG0PMYVsoswQdzkZ+pRYWlh9EnT7mPc+rub9X832e/zsZ84IvpvX5mOuGnUo8Ji\nSn+T1paOKiSig3OUPxwz3z8/n/soYywr3u8SwcaGhc3WgV9t3LgpzNai0sq895e9VwgPpc6Nl3+H\nM2YdcGBEFINpZRv/PiPKH32trO8ynQurB97XvHfd8eImP1uPisvzd8oys+R9jUWif0iPyWR97+Pa\nGWTH8HTSe+vqnuKmsI9r/JCZXYii6C0zM+fc75rZJ81s5IBVmlm0J//BfzPRxaKM37qjHAXJkIIk\nj/m5APOHXn4uHDOi+KHmr6fF85AO5RYZc4KWOq6POfl76X//pUt28IwdN4WlBTv13/2T0WfczwSZ\n6j/xTiUrP0zpIfi7eSoMf5fzeVLl5/N9Zb0LyprwHeAE+Z1/9LnpiJvFRTv9y78w+oyJ+o4/RkXM\ncwHedFTCxnX9XHp+j/Pj87s+tzOVk66dNfnO7H/SyIqruzSBvvo//y8Hc6IkY8VNaWXeHv+NT99O\nZ03ufIaUl6PvhpSfz5j8pU0OsyaOyUlo+vGDEIMurWxZ98H5hRw+C8EQg8r/PtdhPsedKPKdv/8v\n70ZfYzZm3BSX5+3sr/3XI0/G9wVTABoHHPVLwwHVVxHrJAwwv1AMMT+M83PUd+z3cU5eKz5Dnu+D\nxzO+duY/pMY7H54Mj33zp391T3GzH4nFGTN710tfvvU3wDn3GefcC865F4Juax+XE/cIY8dNuKu4\nERPETVNxI7LjBsaobcWMMLMx4ybcUdzci9z1RXpRFH0+iqJnoyh6tlCp3+3LiXsEP27yM4obsTcg\nbhqKG5ENjFFzihmxN6CvmVXc3IvsR2JxxczOeemzt/62ZzLe+qTKAyISv0T0WpIlFfz9If/Bjc5j\nyUWuj1/NBZgutvEVyLBIZfX+WRKU6Vo9PFdY4jS9gupj2RLnG6S8pjsa/ey+4ybzlXE04vOd0ixz\n4Dd6Wa9x/OM5pPjc1LYJ+NV6ODo/KnBBMyqFJEiJ1/YsN4DvZsgzDofJ4sYPcu5DCnxf3meSSHB/\n4ioh5ZMko5Dxat6r06hoqTj+GYOrn1+bsuTC/8wxxWQ19fHTHO6rv+F241flaXmJrofys5qiQB10\n3mvnLOkBlyUI038L47JVyvEgx1KRE9U2npskE0uVJqTXuo3Ua290arc/D6icw+GRGW2NFTdRlC6/\niWgs8A8dkkTCkWg46uPAEPCYRGmajqRLdShdrgzw2mPr5uN74esGg/TpJktJcsX0GM8XvCcoU6c9\nGfuJvm+a2ePOuYedcyUz+2kz+9KBlErcyyhuxCQobsQkKG7EJChuxOS/IEdRFDjnfs7MvmLv/bb1\nhSiKXj6wkol7EsWNmATFjZgExY2YBMWNMNufxMKiKPqymX35gMoi7hMUN2ISFDdiEhQ3YhIUN2Jf\nE+T9kpCNkCjL1945lMYkNH+sG2ZNclClc5O8xdf6FmlBKmv6WF2Wo3IP6mS70iV9onftIumbiy0s\nWPtkPjU/qGDZym0838BbO8D3fGwZx66O87Ks1liX2qN81kX53ydrMOtg20Vl/G6uRflUtnwH4yis\nxt934WjbMDNLaqkztGOONbdp9nXHCJemIWeBmafLTmiKu3Qw27hRW9ssdlhuQP2TpzvOkQXckM6d\n6qFsd7CgY/2z7zPLOveEUDbdMo7rJc3/mTX4d0kmeMBEqZpNtjTzdaes1c3SJHNkhqS3TWqUveef\nxKClPNltZVjO9QMMhJD6k91O5fbn03M7kNcJUDT/d1fQ+ez7zdOQ/qmVv4b0n64/CWnzxua1Ni52\ny9GgNZzKmHlPm+tSdOGOHjyIMRpT2IotoOcoX6a2btEiBnomh914mhdV0pXvrS5OCSszuHAmn8ey\ncmyst2I9eamA12rSfbU2cFJWrGOfOdguQ7owi4u/Bu140lasYR5r1zn+94q2mhZCCCGEEMJDE2Qh\nhBBCCCE8NEEWQgghhBDC40g1yCwLSWhk/a1faSqfJ09Z9h3lc+VIw8y64dDzD66s45fz5CU8qOZS\n87vz5EtIOuFS0/Oz7OF3K2tdSLsQdThcZ4MGaW0GXA9x/qCe7g19bEnzOs7wjExokCmdoy2EjbcM\n9g7P7eKhrH8t7rCenI4nA0uO4d4CC0hjggbp1mqkKa5STNfwYmnbfLI/JXtjHhcSu2uzntZP87Hl\nFO25WVJ/3qO2Svn+kPISmmLWDbK2l6/N+Ien+V3fCdJecz04ig3/2Tsau+yDhZ8LthN33kMa0vNe\n4K6F8pPe2Ky3peO9vpz1ylkaS76PPOlBq2XUcO62Yg1ytUCDJ/F0Be2BHyndhPSA9LcfmL0K6T+/\n+djtzwuVDuRtdlGnmpvmQctrL27baMiH7v0+2A+4QNpeoz1KymVsr6Aa1//iLA46WR7XORpcb3bw\nYitVHPTK+Xhc4bYL2O+Z1m0ELZyO5insgiFtCuHN+cIBnis3bj83Av2CLIQQQgghhIcmyEIIIYQQ\nQngcrsTCoVQiYXuTeAUaf+btnYfkbMISCpYuFFC5YIUOvZL2ZBCNd/Hg3hL+tF/o4iuP7Ufw5/3S\nDp67t4A31rgavyIpbeGNRXn8N0vtjTVIdx49gWXpsO0bWUZ57/kSEha2cDpSwc3ksLwGYOsdlliU\nSVpAr4GGVXqdRa/OCpX4lVKwTa+A6DXmsECvlFjyQnBM+8/DkNoqmMVy5slmrFanB4DodvGBCvue\nNRDXWZZf1bQQoXVkopgBy5M82QPFVI5lDyz5ourl/BzbvHmvAElFlbC7DEmileuThGuevsAWdF4c\nltgOiey9HMVsP6xgPlvQkQTJv69knU3FluVjwa/K82yn5kkd6iWyqSIZRKOMsqZ+iP1BP0jvgLve\nVr38CG7u1iDNr9nZ9qpKZfUlFWZmoWcD98qVU5C3vICv1dcD3Ep6SL+7LeZx6+l3OouQnivFsoqN\nLukFiDQLvqMmlx9t85ajrep9mQTHWJElFAS3HUtxerSlc7cTj0tzZeyoWGJxtrYF6U6I48LjMzcg\nPVdAScz3m3GssMSit40xxrK0XIP6pi0cT10Dn5+o7d0nKynZGi9tjpCCfkEWQgghhBDCQxNkIYQQ\nQgghPDRBFkIIIYQQwuNwVacR6mATcqLEtqZ3/myG2lqzpBaXNSnlTdT1DMukUfHkL80HUSvDNm2s\nGQxQ/pXQG85fwGvXL0ocFfoAACAASURBVG7H353Fa+UGpD/a3IZkZ3kF0qxX7M+QXtGT7bBuNav+\njy3ejSU0a2wHSLrIHOkESyWs4Lkaaq58W6LCmfS9vB9toP3RX1x/BNL1EmqwLq/PQ9rXrQW0Vexi\nDbVlAekbuz3SGJP2dEh6XF+rzXWU3Hp9SnEU8xwLfMu+Jo5tmqgOCi3akpktvtAtKfHs+d/Pk3Q9\nqJP1400saHeZNMZkPVhaxBj113awnVeW7rVPFlNRwPdtlB4dG6nbfk8RqVtN05oGP816zjLplUvk\nLepbYpmZRUVsm50+bbXrPbPtLq2LId0qa477/Tzl05h2FQcxfwwLK3hf63Sf/34bt47+xyf/BNKL\n5Nf16KmvQvovOw/f/vxHa++na6EmudmjwXeKYD2/D+uMB179s3a5QHFTpPxBynXMzDodjI1GIx4b\nbjRRL86xvtHBONjtYH0/uYwaZObS9kJ8rhuzmJmxBmrY4U6SxupdWnhWiE/IVqTFCnmmToh+QRZC\nCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC49B9kMGXl/asHRZJF+VJadgTNql9w3RpF/9Q2qHtF+t4\n65uPxRdrPZguxk1Id0mnV97Af3eUt1APE8zFuuPtR1Hzw3rm2aVHIR2W0j1pK5tYlr63FXVE+6S6\n4N4QHfPWx2m7eOZyrEHG+ioWUf/1yIl1TDfQl/on575z+/MHSpuQdzKPjbkzRJ3wF+uXIf336hch\n/coA9Xcf9bxNv0O+4H+w9RFIv7R1FtJv3liyVBLb4Mafx9gVdbqIyDOYfg5wtFbAl+Plu+RNilau\nCV/2ykb6Ggjuv/y1Ar1FWjeQUi4zs6hEMTyPhSmVsL8peprGFmkKwyBdm5rbpSGCnzXSJA+9svHW\n6exdn/BlnwKiyIF+n3XFXfKY9Y+dq6Z7zC5USJhOtAPUjobkI9v32orXENSrPUiztnSpgVsM92iN\nwmoN+yrXjM9f2sJr9coYQ3/yxhOQZl/eJ+rXIX26iP1k3guEE2UsZ5d8eDsDCqIpIl9I8UGmccbv\nU8tF7BxWGtjZnG9sQHowxLYr5/BBu9FDnfFqK9YCh9Q21147CekT38H8pRs4Hu40z0G6dRpjdvsH\n4+9XaJ1G9xz2U41FbOs6rY/o9LGty0W8z/XN+D6L1Oel6cHHQb8gCyGEEEII4aEJshBCCCGEEB6a\nIAshhBBCCOFxuBpkM3PDWBuV0MSmaNLYB5m1uuVtPFdQxbl/tIxamdwANVrFdpxuvI3n5nLNXEG9\nS6GNOh3W5RQ3UJu2/dTM7c/tU+y3itdq9rGJFl5Dj9NcF/VLrYfI59Arey7M0BxPq9Y0MtQ+8m2Q\n56evn2XP3iHpxdnvdlgjvTidu0jG0yfysY7qdAHrntkakj48wrZ9sYe+x0+TpjmM4rh6pYca42u9\nOUhf2cY0axYTPr99zHeluKys8U6KYqc0cJxZ5Hsbc9OXRz8PfEusny02SV9HvzVU10hzSJfy1xJ0\nT2BmWKXvhlQYThOtJnqrD5ujdZusIeZ0YZfihEaMwQI+D87TfEf50Z7s7+WPLNaR4VyU8Dr2oSEL\nPGtZH1slben19gzmFzC/Sb7HIfVdJc/ruEe+5rMV1CDPlnHM+fD8u5Be7aFH7dMLqBO+0o77j7fX\nFiEv18MgiLZxvPuT7z4F6ZdOPwDpHz51CdIFzx96toDlvkT9bzic3t/0wpTnMiTNt6/9d+R7vrqL\ncVIhH2nWZbO/9moLv9/sxnHV3sUYy3doz4Y3sf7dgHy/mxRnXXyoW6fjMbD5MG3SQF7FC7SnAD8P\n8xXM3yJNvu/vzD7/yU0eJmN6o00IIYQQQogjQBNkIYQQQgghPDRBFkIIIYQQwuPQNcigO6bpeYhS\nJvOlNUMqKUtMmmdQS1O9SceTBmjubdTSLL4c61ncALUzQQMLFhWw4EEdzx2UsSxrP4h6r/6ctw/7\nj6DOdPcSakejPF4rF6IOJ6hiOo+3BVrrwGVoAg89GvaIM9RHs9SLJYP+Hu59Opj/SUgyqSFpMC+T\nlpc9KDte0P6L9gLksX754toJvHSA+fOz6JP6t069aaO42MJzXdrGa+9ukUifHpiI971nf+i8V6nh\naI33VBMlvY59CihxA//hPOWVN/GmS00MukEtXRfcIa/j3okRB5pZcRfrm0LOGpfxXINrpM27TLpB\nz16Uz9WbJw1+AdN97LosmKE44efLz6Nnaxo1x0wUucRz69OnZ9b3jS4W8IbbpIssUf5ODvWgPdIw\n87qBXifOz5Hn7lIVvXMfqG5Deo4C+om5VUvj42feuf35v5/5Mch74SZ64W69hV66gzmMic23UMP8\n/+6gv/vy4s7tzwGN06wHH05x35PPjy5cPk/9hVdF7D3+wMIOpM/WtiDdo8nQt2/iepStXewP7K24\nvusb6XOA7fO4fqH5IPU1dRxXSrT2q7virV0p0x4DM+hz/NHltyHNfs55Wvz18s5pSF/ciuOqVsG6\n75FfeWINzh7RL8hCCCGEEEJ4aIIshBBCCCGEhybIQgghhBBCeBy66tT34mUtI/t9Dj2J1gBlS9Zf\nIoFbHfUr7Udw7l9Yx1vNhaj/mvFsIktXUAtqpEG+8RHU6RRaeCOtM/h19kzt/UB8/n/0yIt47nPo\nYXi1gxrYb373UUhXbuB9VVh7PbzzZzOzYWn0sVMF+yBn4euIWSLJmlT2A25hfTYj1Fy1drHt33w3\n1t/l1klzuIkxOFggPd0SarK2m6gde3XnFH7fE5C+u46eyQPyJrUdTLO/bVTjYKD8Ybqm9ljgzKLC\n6MDxvYjNzIbgmYx57Ffe75I2jyywc33WHJO3sdddRedQH9rfYR911GU68jOfv4Dnnn0F1zXY5Vhv\n6moYY92nUb+4/gz2iwz7Q/N9+j7ux0FzzDgXWSHne4CTL3QBxxnflzdHOv8haZk7PdIvky5y0CYv\n18Ho36+GVO/Ds5h+dRv7jreaS5CeLaLf7a+e+TeQ7nqD8wcb6KH8jdWHIM3PWP2d9N/d+tvYh65u\nx/ddWcZnoVQkXWqKR/VRAz7IFDfskTxoxs94WMW5zCtvo2/0Ww1csNCnvRGiVazP2tWUuKEQa30Q\n42BuBfuOf/rwn0D6W62HIf2Hb74f0iXvvvmezy2hlrpFczDWVn93AydSG23su3xdMT+nrOuedN2M\nfkEWQgghhBDCI3OC7Jz7gnPuhnPue97fFp1zX3XOvXHr/wtp5xD3H4obMQmKGzEJihsxCYobkcZe\nfkF+zsw+QX/7nJk9H0XR42b2/K20ED7PmeJGjM9zprgR4/OcKW7E+DxnihsxgkwNchRFf+6cO09/\n/qSZ/eitz79lZn9mZp/NvBppSdknM99DoUjO07Cwf2e+iXP70inU0szXUct0vYZa3l3yCpx7Iy5M\n8ynU/NQvoi9hUCOPWdK/9E+gbqrwdAvS//mj3739+Z8tvQZ5zSHexxdJ0Nx9BkVEf11ET8r+HFZU\naSuupwJJq4vNu2cqeaBx48yc7/vJQsgi6tJynh+lI11guYL7vRfzGIS7pANm7VLhIuq9yltxWao3\n8OBBneuX/LPPYJw8sIjepcxuL9ZsDViHxl7FM3hu1hQ78uyMWKPsH8965LsoTz7o/iZNV895eU/X\nyfpZ9kwOMAxsMJuujWQNuG/52d8mzfEWXjyinzE6K9RWeTy+dg37J/dK3H8V5tDYuLiLOvhCB8vS\nnxtdbrOk9i+oxnGV75AusMSLTuzAOKi4iSJnA8+Ll3XF/X6RvxIfS/pY7jv6Xfwur3cobpLWnELK\nX6PTO4X92PeuoG41WMMArV7Fc/fnsHCf+gBqlBer8WBx8SaOh8NLuCBo/gKWs7qJQdKlMSmkfQLy\nnTjA+7SWgrWk5TLe9345yP4GfZDTx9bSYtyhtHdRi1ubxTlA+woucOC1LXNv4Lkj8rS/+bF4jPsn\nf+uPIe+TM9+F9KNFvNbzHWy7Kx1c+8Lj62Ijjpv5CunJqfNY7eB6q3d38If6jS2Ms3oD68XX+B+U\n5piZVIO8EkXRtVufV81sZdSBzrnPOOdecM69EHRbow4T9wcTxU24q7i5z5ksblqKm/ucPcUNxMxO\n+06HiPuLCeJGfc29yL4X6UVRlOoxEEXR56MoejaKomcLlfqow8R9xjhxk59R3Ij3GCtu6oob8R5p\ncQMxM1u70yHiPmXvcaO+5l5kUpu3686501EUXXPOnTazG5OchCUW/EqpuBP/odDGn9DZOq39IN7K\nTg5fMYW79GosYVcU//xff5P2a6atpcsbmM2vQPmfHR95AG1yfmLm5duff3sXX19d7C1D+v++9EFI\nb26Sn1SQ/vo717/zZzOziLeevvv7eE4cN2m2Y5zjv/Yp0Ss5fiXE28Pya9Lg5mhJhRnGLL+O3nkU\nz1V4AH9lePYsxsV/vPQSpL+88QFIN/vxq7hSGV9XDXL4KozvY0hbuHI9hDmSaIR3UUcxPpPFjUOp\nBL965Oc29OQBPCT28c2iuRXsIwpkRzW4igNmHt8OWsmLIxeSJR/3ix9EidfSDMbR6gbKJoIqvQJf\n+aiNYvNx2jr9FMVNGQvj+DmkOIk8uRPbmCUe1Lu/bfDYceOcgc1bSBKSpIzC3fGzWfK1b0KqVKJz\n0TPMllzhibgDP3Ua7bhWr+Lr6TzZwJW3qN+j8fPmPH5/ZzHu9/o7KAGo0HdD2ua3tUIxdXK0DMfM\nLJyNY4weUSsWMf4Kh2PzNlF/k2bzxgSDuI4i2r683cT6Lu5gPkuXerSEsLuElfhTz37n9ue3Oji/\n+NXtvw/pb3zncUivfJ2sCk+QfS494tc+Eku0IlTt2HIV+603N3Du030DZbBRHdu6mTYHoMDJUZrH\nu70y6S/IXzKzT936/Ckz++KE5xH3F4obMQmKGzEJihsxCYobYWZ7s3n7HTP7hpk96Zy77Jz7tJn9\nupl93Dn3hpn9xK20ELdR3IhJUNyISVDciElQ3Ig09uJi8TMjsn78gMsi7iEUN2ISFDdiEhQ3YhIU\nNyKNQ99q2oc1VqxB9l1BZt7ppx7bXULdnSPHrPI8abB26fsn48UZtRcv4blKaH1U2kGtTEhbNhuV\n7WuvPwbpv7gQbxedI7utShXvk7eVrDVQ+9giq6CI7bt8ORjLlUlznCGbOlJAY0QFZf1RwdOtnWjg\nqnS2nmkNsPHWQ9J4s4yQYtbXde88hg1fewi1ox84eQ3S52qoI/zazhOQ/vYqbgPc3Iot6NimjeMo\nDEjPSHHC745cn/7ga08T+wvfPbuuAyVCPS9vZc/Wa75MbchbVPOW5JTda2EcJTRxVL99rz+iHVct\nR05Wp0hz/KETlyH9YoRWj6sfIk3jmfgCUZGeeepv8qw5Zvu/Pvnf8dbt/vlpzcNxIIrMhikdYUDP\nFX8X0rTVdK7Ii2zo3Ccpu4YV+HfOv3X78zONq5D3ldLTkH79bdqmfgbjs7ZKGk3SLPs2koU6BmRv\nmbbInqUY4VnFEgrwq3Rfdc9qs5DHOgqP0Zb3vs1bRMHAmtiyt2ahabjOpUC6694sBkp4HutvWML1\nD59+6uuQvuKJlP/oAsbJwh/iotSn/wLHqOAizoUWn0KN8rCGcXW5FM+NrtL6hpsNHFsHpG0v0OMR\n1chTkvpv/3mi5VSJ5Q2TRpG2mhZCCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC43A1yM5ADJKwpmON\nbBgfMCzjXD4sYbp6E7+b75LmZwvzG1dQx9NeiXU+hadR+9k6jTqbtR/Ecw/naGvpNdQMFa5jNftb\nPrPvaKuCupyVR9YgfWMNPU9zXayH2lXyTPQky4U2lZtbf4rlXrCVMmuVaB/ynucruVVEfVef/IAH\n5NU6HHB94vHlTfL89HcCX0J9+MceQP3Wf3ri/4P0/7mG/rRv7qBxZHMVNVugEyZNW0ieqvkdijn2\n1mW9JP1TOfA1zXk+GJPTHDdAxpbZQ+8+E+sjFlFHmfDZJO0uVbf1F7G/mZuNO4HHFvEZf4e2XF2q\nNiF9vrIO6d15jPFegG2/7mn3Itaak65vuIl9XVShO2EbWv6Jxb8Wxw1vd373fZDHxjncXpr1yCXS\ne/qwRy97Js9XMYZmithfnCij1vwDM6g1f7p85fbnMwVc3/D1zUcgXZ3DawVVbNed87SvwAquzfC3\ndC5T57FBMRN2MN5ypB0tUp2VCpiuluJrdQe0pobXm0xj0NzC90Hm/iHhie2Rp/otkad6t4751RrG\nzZk5XHD1/I33Qfr1l+P5zMxbOJ7le3junQ+hdr0xU4V00MD5ybCM5yt6Iex6dC3a3jysUpw8hjE4\nW8Y+szfATjnvPV/d3ugt4M3S91FIQ78gCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOFxtD7IedSF\n5Hvs4evp2XJ47NZjtN/7CvknzqK25uwfpf9boHkmPv/aD6Je6/z70XPylx/4FqS/3zkN6YA0sV/+\nxocg3Xs41t7Mfgs1PaVdrIOt6yuQzj+NesSAtI3dHp5v9mL8ORpt4fle/nHRknJTkq7SNWJNG/tI\n18vp5qzld7D+2JO2u0iaOC/sfuKJ70NeK8BzrQbonz0kEey1TdSXF3ZYL+YlSDQcVkiTf4O0pekS\nLQtqpA/ztaSsO+VzsQHsFAExzR7h7J3pefhG5IPMPtPBLlaCI+16bh7j7MMPvQvpf3HuD29//svO\nw5D3SvUBSH9z7SFIf+U6eple3sK4Yv0j5DXxeShtYLlL5B8fVkmz32AdIWktK16a+xPWj06lx20E\nOlfWwLJ+ttOLx4qlBmqIQ/JBfmwWF8p8sIEa4w9VcM3Cj9Az/XI/1mh+cQfHFNatd6/RvgBz+BBz\nfD+xjLr2+XJ8racaq5D3rZkHIb3RQS9d1sDnSYvN6U4/fpZYY8wacNZ5TxP+czektS2sw25347hh\nfWyPxqyZRYyrRxY2IL3VQ53wpTfRUHv+1fgZzvexftsr+Hw3z1Ef+TfnIZ07g/sKDDawrM7TRz/4\nAMZUpYCDaa2AfeR2H+9jQOuFun3sc/3YSMRNYr3JZGOUfkEWQgghhBDCQxNkIYQQQgghPDRBFkII\nIYQQwuNINci5kHQhbNHpSYH7ddSjBCh7stwZ9NAb9sgL9BnUr8y9hRqVvufRVz+PQrwc6Ve+3UQN\n1tfeRQ/Kk7OoEy60SZ+4Fd/YiZd7dCzqdJpn0Qs3DEiXeo00zDukmduNNVthifwrUWo9vX62kaFf\nK8vQWNrYitueJMS2voX1WXoJtXoPfBt1UaV1jKvuSQy83Qfja/3bl94PefMru5Dukzb91ZuoL+/v\nYoMUUtqjdwrvzPUwxnrzpC2luHBsb4syVqzjMj+nlJ7WuHGW+hNAwqc35T7CNvYn1RMYFwXyMq2V\nsH0+unAR0s+UYr1dzV2AvN0h+hq/WVmG9NUmatV7XYybcgVj2K3F+bVro33SzZK6bMe2v6zb5voN\nPO06eXO7xMltCnGgO85T35+n2K947TxXQu/hToBjTjmHlbkZYN8zn8PG+G4fK+grzbh/udpDbWgp\nj/GXP4HnCjdobcVC+lqMH1147fbnx8uoQW6GeK7vBmcsjXoRr8X1UvKenT6Pb6Q5Zk34NOGXLZdP\nL7evOw5Jrzzo0joB0i+/fhP7g8UG6oId+VB3TsV1evJFjJO1Z8gvv0P1+wzOZZZpbrNVwX7O9/o+\nVUev7sUSlnOlhPnvdBYh/cLqOUtjMIjvK81n2swmXmClX5CFEEIIIYTw0ARZCCGEEEIID02QhRBC\nCCGE8DhSDTKTI1lUUI11I+Vt8ucjX+SEX2IZdTj9BdQ9rf0AeTA/HGtrWHd24R30Fby4jpqryhr+\nO+PKLGoEa9dG6z9zfdQEbb4PdWnVm1iWQhu9AllD6EjXPfDqcDo1f3vAmblSCGmfKCDPyWqsi3Kk\nGWQ9VwElVVa6idrS6NsvQ7p+Bj1qK2snbn9e/D4+Tm//FHqTfquAukHfd9cM/SrNkr7g20/Gn10Z\nNW7VJdR3tWdQxxpsYvyzD2qiTospvpGs90rx3T1SXPp9REUSs/u3VcC8QhmfU9Ycs992axfr/18N\nPwLpmtfZ/Wjtdchrhvjdmx3sE9Y3UUcfraMmdNDB78+/FX/m/oG90YMati3JZBPrFljLjgdTnEyl\n7zETWc7TveY41FM0yaU89i3sT/vvrzwK6Z1dzP9C8CNYEgrdxitxO7dPk7dwj8ZDWjcQ1bGh2DeW\nNcyLXsf4dh81rw0adJ6aR43y1Q4vaEDY/33g+UVzuRLa3SnVIEdRUkvsw6UOfJ0xe/SSp/pggH3L\ng4ubkP47y29Aen0ZH9q/Wjl/+/O1Cq57CU+hbr5EmuIfOove3A3yLn5r9wSkL63HOmJ+Vnb62C8N\natj5vLmzBOnEnC4/2gM7ytjkIeKFSntEvyALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4XG4GuTI\nzHkyEvbQZCmOL0EJyqhHIUtJc5dJm9vE40++jBqr3hzmt3Zmbn/ePkn6wy4eWyRPWdYBz6EkKOH3\nHHpy0EITNT2VTRT5sda6dQorrbKJ5+b7SvsnENf3cIr/uRSl6LvYF9nXHder2Di8v3tnBSshmEc9\nZ/kh9GIcrm1AOrfradfr6JG8svQQpLcfxmuTnWhCDzqYId/qahzDvs7azGy+jtrp2Rpqy9arqEsb\nUj1wMIS9ON+xPvlYaElvkSY94zxPl50nD9+FuRak2z02ESfWsHHXt/D4fzf/xO3P83nUj//pjScg\nzVq8YRP15LVV8lkn8+9c4N0o3XNQJz0oaY7HXrfgn58lg2kd/BTh61wDivWQ0r4GeZs0x5u0XmT3\nMq5NmX0Nn8HyFq1JeANjzvf+L61h3tYzuL5h5zzGRJs0yLUa9os5CozXu6dtFNf7eB9rPQyaG+0Z\nSLOueD8c5LkOkyHp8XPFuD2GtIYm38AHeIb6ctZwv9LEtrrZwTUK67tx+wTzOHFaXMBFOHXyb3+i\nfgPSZepcbnTxWitzsfc/e1gXKL3ew/GyUcKY3KU+th8c/pK5KZ4SCSGEEEIIcfhogiyEEEIIIYTH\n4f5m7VBWwduYuiFt47kVf84NMG/mEs7tO0v42mHubXylVLmJUoaZC/jaon4mflWw9Ri+wuwu4bWL\n+EbUhlSLax/Fa1cv4wEgySAvn8pNevU1oNcUbXx1G+Xp9Wt+tN1JSDIVfqXvRruoHC0uspxv88Zv\naulVbc3bapetYar0CunaHL4uvPQJrN/GOyixWPlLPD53Zc07OdrYsE0bS3GYIVto0fNRXo8brDeL\nMdVYwvherpJ/HcFWhlv0Otg8u59eF5+HsI+BM7WSC9qiPPGGH28LYoy3a64UsDHqJcxfa+Jr5qjJ\nMizsr771drxd/YsUY8NNjMHCLm0bTnGRI6u1kJqydcbb1pbsv9imjfPDKh1PfcSwSnZjrbis/DLc\nBXuXf00LadZSZmYnarHUoUI2byyNYdu7+TexL6q9vgbp6PI1LEs3HrPcEtprNWax71n7EAbBR57G\nrc5PVrB/KFAghN5AzZKKrT6emyUV/Cw0KixzI0mQVy3TauO2J7yyJ6QgtK19xBaIHmyPtt3E+u6R\n7dtuD/uLmTLWd82r/wiVOImt039w6V1Ir/bQsu9GDyUVayTnuHQ5tmorVPB5mJ/FiRP3oT2W/RH+\n1tJmZsXiaI/JhF3ghPrRY9BFCSGEEEIIcXhogiyEEEIIIYSHJshCCCGEEEJ4HLpvhi8xcjQ9D0u0\nzaknq3Kkc2Tdb6GDmpPuPJ58mCf7tDOsNY1tslyI4sRCm23e8Fq7j2BZWHzXPUXbeH7H21rzKurO\n3M2bkM499jCe7ARt11gnPRfpEwf+1rHHw2UpSeRs6OleefvoiLbm3NmJNVusaZubIwH5HOoAgxLr\noDDQcm3UTQUPnvTy8Fztk6TVpZjt0Hax1Wtk98NaU+/ShRLGVJG2it0ZYJx0+xjTrQ4+D0PSaPnW\nQ8fVWskMdccc747slYaduIFwhYLZFmlROa5mq/iNzafw3OUiNmbR+36L1hVUrmLcJOSMpGVPWLNR\nvzr09I9sLTgkO7uogeXMldP2kjYzspwbluJrOdZZHoufYxy0bZ9ipEDP2XYvfs5C0lSy9rZJ28Pv\nPEh9S38R0sVF1HcOvb4pR/agm0+hTjU4Sf3UkCwmKX5XOyhOvepi7empyi7ktQPsO7Zpa3OmRXZd\nadtJsxUYM619kXNmufzoZ4W3LIe1WGM+F/3BeNO2otd3Vcs4RiX0zgPSO4d4rU2yZru5i51PdTbu\nB9lqdLGKY+/NFsZ3s4OdU6+NfQuP+4N+XLY8989j+1PemWPRZQkhhBBCCHFYaIIshBBCCCGER+YE\n2Tl3zjn3p865V5xzLzvnfv7W3xedc191zr1x6/8Ld7+44riguBGToLgR46KYEZOguBFZ7EXMEpjZ\nL0VR9KJzbsbMvuWc+6qZ/ayZPR9F0a875z5nZp8zs89mns2XxLJMhOWfnqyE/TcLJBIkiZXVbqIe\nqNDGdL6H6c5KrKOCrVnNrLQFyYR/cGkTb6S8gdU6/ybq+irXPS3OIvoM5s4sQ7p5HnU6nUXStVJZ\neJtZP59123dZznVwcePMXMELANKj56ok1vVgbVJCC0pbNv/AY29D+sUmbvvb+odLkF767v/f3rn8\nyHGVUfzcfs0zdmzHGJO3UISSFZGyAMEOIYVsYIVggbyIxIYFkdgk8A+wyo5NpKBkESEhJVKyQxCx\nQUgRURQlhhASQFFAtuNHPOOZ6XdfFtOmv+/c6aqemXb37Znzkyx3Tb2+rjp1q2bq3HNHB3HpJuVf\n3yANNmmY8Gt8AVAGLeUiD0zW98qK9xhy5uq1He8Na7Ypy7jLIdjjS+EMyTucezw93UTKkuav3PTf\nI2yNLpBY8wvf2vTHr37aN0DNqp/POttp+ZPZbRn/3CUyBtPhXbpOfR6oW4L1/QJpNrGlftL7YpeX\nfJ1n7/L5uOwvr1nXvQAAD7ZJREFU3aRMVtRpXybLNPIQ8HeuvZniPSp6nytdV5wbu1oftR9VuknZ\nebsL+HW3HvCzK12vkeYXvC7s5jun/Hng/OqvPHjZTW90/Hn8+Lpvx5o7ft8V8z3fI18wtwd98mmz\nT5g9ttUatYtmukd/syvzJB+S6T7bWEg3NWoPbJ+PKnU24XvWgNrbDrXlfLxXl8gLb7zxDfJJX/7c\nZ1j/mRqXWHLNJn1XTF+h5jXvV75SpxBm6jsE6g+BLjeEfr59JuA6KlPSTelfkGOMl2KM7ww/3wLw\nAYB7AXwXwMvDxV4G8L2pVCSOBNKNOAjSjdgv0ow4CNKNKGNfHuQQwkMAHgfwFoBzMcbbQ/1cBnBu\nzDo/DiG8HUJ4u9fc3msRccQ5rG76m9LNceTQutmWbo4bh75HbezstYg44ugeJfZi4gfkEMI6gFcB\nPBNj3LTzYowR6ciit+e9EGN8Isb4RG2F84jEUWcauqmekG6OG1PRzZp0c5yYyj3q5Opei4gjjO5R\nYhwTBeqFEOrYFdArMcbXhj++EkI4H2O8FEI4D+CzifZo7V2VsbN2p+sjD0qXbHrsX+aM2QHl2a5d\n8fP7Hb9zmxfMecwD8oKuXfL+lsaWr5wzmdf+5a45xKWRh+jmV70XrLtGfqO7yCfJ1hqOGiWfm12e\nPYBJLuyUPYJT082Aso7JB8ieZEufslivXveeKz5e73xCxkDe3gr5Qe8f1bVz1muqfYZWpnPH/nE+\nt61z3psW1kbTy3SyOK+y0/Mb79F0qBaLwc5NfGhlQjokU21vTGkV8rQFii21fuVIuolkpOxukaeY\nvLzL5Dk8fcL/hel6HN1Qu3f7c1dt07k8SfnwJ0gXVGtlzXtfB9uj9qbfJ88gXTuXPj/h9930fsdI\nftPER+gKo7xbbm+m6GWfnmZ8DjLnxHbpOuoar/pmy/t8e/3ivz/11vy2bz7q5w/q1JafGfnHuR8A\n57vfbHmP8k3yjvc+pQc6vp+aXQ/4Pk11RfKOhlXq59Gg6XpBn5EZ5xxPta0poOi6q9epfxT5Z9eX\n/fXc5n405Olepzzu+9ZGnaguXjtfWGdnwz9o1db9vmtU6xL5nbd2zC+YdCo5Uz0s0zSd+36NvO08\n/oE5hpVacU79QfOzJ0mxCABeBPBBjPF5M+sNABeGny8AeP1AFYgjiXQjDoJ0I/aLNCMOgnQjypjk\nL8jfAPAjAO+HEN4d/uznAH4J4LchhKcBfALg+3emRLGgSDfiIEg3Yr9IM+IgSDeikNIH5Bjjn5CG\nQN3mW9MtRxwVpBtxEKQbsV+kGXEQpBtRxv4G9T4sEaiMH6488WgGY4Tq8zyO31wm33DdT7dOkgcz\nWX/0ubHp/SrNc7StU96ZcvrvPluUvXbbD3tf3+aDJm+Vs5/JKtOjPiMVb/nZwzdMuZ2t0QKpB7Bw\n1XwIcL5j9iKxL8r6j3hel/J/B+TfHLToklgnn9QK52ePpgfsz2Sf4CnvQ61R3iX7hh89fc1NL5u8\nzBZ5jnd6ZJQnkmxN8sQVySiwP5G9o7O1DU5O8P0cIl0sNfIku34M9BWrLb54/PHf6vi88i3K7Kyv\n+QvXepiXL/ltsfd05wG6UMm7x+cjdqhRMVnFg7af12Y7P+ufGxg25bEv0K1Kx+yORtpOi+gyVMu8\njDZ/vEYZs2dW/fV+92rTTa/UvL/zypbX0JdPXXfT9hrv9v153Gh7//P1m35bvU3fPtQ77LF3k17+\nrBG6h/crxe0B+9wZe4xZIuwBzxr7PVg3SacpsyznStOi7a6/JlmDPL/X8NuzvuNmx/cp6FL+dSCf\nMN8/G+R/brb89pxwqJ8L91+I3Hb0OU/bb5rbIn4O8OtORzcaaloIIYQQQgiDHpCFEEIIIYQw6AFZ\nCCGEEEIIw2w9yPBepzIP7MBU11/yM/s+1jGNYqXp7nqx56phoor7lLl84t/kw9mk3MKm9+V0Tnpf\nz/Y57xezvuAu5RxXW37fy9e9l4Y9yJz/XOQz5tzphcJ8Mc6o7bXHe9wSjzF5MPlXxNDyP6hvcAio\nX99617HiBV0740/mo/f4MO5H1n28ZoXMe3UK6r3eHWWX3uj6HNONjr8g+uRr4yxj9gUmvmIzneQg\nM7l61wFE64Oji6O/XPDFaBZnDVcTzVHOKbc/8G1C7cbIuxfZq0cSZe90damoIwdQo1zUjvEJxqbf\neGQPYtm55j4TjF2f2/cp52XfKawnlr2M7EnuGC8we0FvNb0vuFIpNmG3mv5cvN/6kpteXxn1dWlT\nf4UlOucrq75fzA7V3a1TLW1q50y2cWgU1722Tn1wSEPLDe+1LoKP76LC34JzkK23ukf9Yji7P5LN\nl/uusE/75g5lXpt7AffBqS77ZxduO1Yo53iFzuXODj0sGV9wdaV4291O8eMne4yTafu5rOG6UznI\nQgghhBBCHCf0gCyEEEIIIYRBD8hCCCGEEEIYZu5BtlYQ9sRWvZUJ0VhW6n3yZ/qh59FdJV8q5QcP\nON6TbHy2Fs5BZq90b9X7eJpn2QjsJyvjh55H/Rb5CztjFry9afbM0rY5GtAuzxnUC2v3Yj9Rgb8o\nySZmOuTVrdF48ORTHSxRlu6t0fqDBnlcad8Xr37RTd9oe5F2B15Xt9re37XTHnkU2c/YI09ij/Y9\noNzUIs8xAH9MBwv8e7QzqtG55OhouyxJqrZVnPVav0XnnrOIw/gc9t4qtTc9v60K+eL7lGlbvcv7\nArtt3x7F7mj9yk6xibjSZZ82NX6cR89+ycp4z/ciEABUzXfol2T4ds11x17QCuUis3d0Y8t7lNmn\nyn7cgVl8jbyhyzV/I1iq+fN8Zs3fMFs9rxHus3B6hW6wBm6n2rwt9mn3ijVX5Dsuy6HOhRiLv0dR\nnnOlWuzxZq9uh3XB/Un4muwXtN8cuuy7tqDZ9m0N++yTds7cT9lLPahTznHJeAYJczj3C3znE0II\nIYQQYvroAVkIIYQQQgjDbC0WIbUIWJJXnvbNA/91nd4M1Jr0eoujwCgWji0XHTMaNEfKte7xrxEq\nlFrDNof6NkUD0Xe264ck0onqbBTPZ/sHv4VY6Gi3cSTDdtKki5oqec1b41fI9Ip5zc/n1982Zo+P\nPQ/bux38K9VP6dUX2yTYBuGGJOX4KX6tlgwHXTI/Kf6ICKfs/BvsIQhdPj60MGuuZMj45PCa5avN\nsgg5ast4mOANsm/w2iZGrsLrsmWL1yW9lw5Pb483S6ikDc+BiHJbxaTwcND8ejq5JskWFZb8jaXd\nHeV98fDEqcXCT59s+GGuK2QdW615y0bPCLRHIrna9MNY18lK0umQBYhepfeKXvkvKCGU2D8OYQ9g\n60Zi5eBNsy3FWjiS+4BflS2JHNA3oHMbWxybaopheyPZFwNZS0rtNMnDT4Gdq2zdCTl6ShVCCCGE\nEOIQ6AFZCCGEEEIIgx6QhRBCCCGEMMx3qOnEO0PT9vGdvbYcncY+3w5FiHA0Cg3faP1wPW8VTYZz\n5jp5eOj+CnkKvf0Lg/poA0ncHNWVeIhLPMaJzS/PVJzDkQz/SrPD+HnJr4TsNWUfFHl72Ztqhwnm\nebXPKRKHhnNt3iA/V6PEuBrGfN5rWabMgpV4uEqWPwqwjqxuKM4vsWyzDshfx3Fpia3VTA+Wiv1z\nyTDXLYqB4+HnkzbDDBvONlga5pr7ZvD3YJJ92e2xX/kI/DmGfZI20qzskmFPZb3hb2JV6g9RLYj/\nKos7q4Xi4YiZHkW3Dcy3afX9TalBeaHNnp9fpQjKw8S8HVmsbuh4cWwbx8Bx/5EYaLpgSPM4YB9w\nSZlcS4MeWGja9YUpOa1lmkzYj8d7Spo6Ak2WEEIIIYQQ00MPyEIIIYQQQhj0gCyEEEIIIYRhrkNN\nM0mWqFtxH8vusXylV+JRMb8qcDRuGYlVtMfzC4auLrbAprmjzAJki06dEn+RO9xlvwLW+QCSL5U9\n4ZSnXUhZ1nBx3G3KNP3kx0EnKLFmH8KnluYecx+HsvZmND+JrOZtUxvAw9H3/YjkSZth7aVlOemJ\np7gy3qe9u8GS6SMGe39rZpq9tDzUdNm2eLhnxi7N2cLblE+7TetutH3HmjIPs62FvaJc54A1tc/r\nKtfho6dKwTFhz3Gyasl89jAnS9t98/XOuchF6+5ZHPXRMdvnjOVQsu9SHczBq66/IAshhBBCCGHQ\nA7IQQgghhBAGPSALIYQQQghhCDEJir2DOwvhKoBPANwD4NrMdjw5udYFzKe2B2OMZ2e8zwTp5lAc\nd91sQ+fmIMy6tpw0k3NbA+Rb23Fva6Sbg5Gtbmb6gPz/nYbwdozxiZnvuIRc6wLyrm1W5HoMcq0L\nyLu2WZDz91dt+ZLz98+1tlzrmiU5H4Nca8u1LkAWCyGEEEIIIRx6QBZCCCGEEMIwrwfkF+a03zJy\nrQvIu7ZZkesxyLUuIO/aZkHO31+15UvO3z/X2nKta5bkfAxyrS3XuubjQRZCCCGEECJXZLEQQggh\nhBDCoAdkIYQQQgghDDN9QA4hPBlC+DCE8HEI4dlZ7nuPWn4dQvgshHDR/Ox0COH3IYSPhv+fmkNd\n94cQ/hhC+FsI4a8hhJ/mUtu8kG4mqku6IaSbieqSbgjpZqK6pBsiF93kqplhHQulm5k9IIcQqgB+\nBeA7AB4D8MMQwmOz2v8evATgSfrZswDejDE+AuDN4fSs6QH4WYzxMQBfA/CT4XHKobaZI91MjHRj\nkG4mRroxSDcTI90YMtPNS8hTM8Ci6SbGOJN/AL4O4Hdm+jkAz81q/2NqegjARTP9IYDzw8/nAXw4\nz/qGdbwO4Ns51ibdSDe5/pNupBvpRro5rrpZBM0sgm5mabG4F8CnZvo/w5/lxLkY46Xh58sAzs2z\nmBDCQwAeB/AWMqtthkg3+0S6ASDd7BvpBoB0s2+kGwD56ya787IIulEnvTHE3V9l5paBF0JYB/Aq\ngGdijJt23rxrE+OZ97mRbhaTeZ8b6WYxmfe5kW4WjxzOy6LoZpYPyP8FcL+Zvm/4s5y4EkI4DwDD\n/z+bRxEhhDp2xfNKjPG1nGqbA9LNhEg3DulmQqQbh3QzIdKNI3fdZHNeFkk3s3xA/guAR0IID4cQ\nGgB+AOCNGe5/Et4AcGH4+QJ2/TEzJYQQALwI4IMY4/M51TYnpJsJkG4SpJsJkG4SpJsJkG4SctdN\nFudl4XQzY0P2UwD+AeCfAH4xT/M1gN8AuASgi12/0NMAzmC3B+VHAP4A4PQc6vomdl8vvAfg3eG/\np3KobY7nSrqRbqQb6Ua6kW6y/ZeLbnLVzCLqRkNNCyGEEEIIYVAnPSGEEEIIIQx6QBZCCCGEEMKg\nB2QhhBBCCCEMekAWQgghhBDCoAdkIYQQQgghDHpAFkIIIYQQwqAHZCGEEEIIIQz/AzEuyVBpAu71\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 720x360 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FKXhbxvd9RA7","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":950},"outputId":"27284587-5413-4ad1-f82e-0b76639921af","executionInfo":{"status":"ok","timestamp":1569206335309,"user_tz":240,"elapsed":6162,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# test(model, device, test_loader, eps)\n","# grads[0][0].shape\n","import matplotlib.pyplot as plt\n","\n","# f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(10)\n","# f.set_figwidth(10)\n","# https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html\n","  \n","  \n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads_just[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  \n","\n","\n","\n","\n","\n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads_just_Attack[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  \n","\n","\n","\n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  a = torch.cat(grads_just[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","\n","  b = torch.cat(grads_just_Attack[i-1])\n","  # a.shape\n","  b = b.cpu().detach()\n","\n","\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(b, dim=0) + torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  "],"execution_count":88,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsgAAAE3CAYAAAC3h7cnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRRt9eZXs+6VlkL1nrD92aXlw70m9spqZXtkUWXE/id9c8Uk7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dNlsekr2VK6AIpXSBL60KbdU+kW82P1lHxuXNd0gleQX/K0jaeu7pOcTPD\n+s/4c1Bhf1C8dsJPlOtsDE3W3dYc3zWcoa6YtZApAjOX4UkbLpDmk/21K5g/pCDt9Ec/l8Z9V5t0\nfuz5Sf6igzppmD1tK3su5wLqV0mTzNr1QQ2vndBqj+O7frj+2nvHbw/ycnWD0YUukEd9jrTg7M1f\nu44x1lnH/sENMd0exuLT3iKeaweTlud2oZgI6yzapKQXcoVtWidTSFdmspY0ax1IWPYuzlLqScWj\nR8E4ZfWPjXhcTu/bcygLTly2tJ24mHduzAlobWEnj33NgNbNBOwdT0TtuLClFq974XkQfneIS78S\ncZS82OisgwobbTUthBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeha5DTtCGOfe58ORvvW89aMNYA\nkk5nQFqb5jnyNS16eq+HH4S8kLQx7VPpnrIhSscSZevPxzcTzaI5Zp48T1nxFpImaEj+rEberr6e\nKVH3d9kH+SBJjZsxNW9wLH+X9F5BdXRMmqFWr0ibKRVJg1XZwIs1LmOQDhr4OOYphjsLceG4XKzf\n4rZN6IhZo5gwbbXjT2RmYcqNsN7Rq4SsR4F1v6wbDlvsBUvlKMWxkOuRxpN0r3nSsrM395D6TW7L\n7krcp/he2mZmYQmP5ZhL+K5z3JBnc5Sm3U7xiZ0qQB+6968lnkH6clgiD2nymC7t4PGFLq1JaMTf\nr97EK3F/MKD9TriPLG3QOEIzAX985XhMrF3hviZDSzosjD5fNMbeCFMHLEhJvw//0Ii8hfn55TEp\nrPLJMMm6Yn/NE/v8Z40brIe2JgUK5Rc8n2XWHGetQeA5W7Iwo+spUd2JAW0y9AuyEEIIIYQQHpog\nCyGEEEII4aEJshBCCCGEEB5H64NMJPw9fe1Sxr7drG8ZkrYOvBbNrEfaufbplHPNpu9zzzocx5pC\n1lyBAIm0i00UBfG5Ev62Gf63kE7Lm3JSJUVj5CXihvLZ9zWhueT69s7HWvR8P127110qpubvnsWL\nBZ4HLZeD0+wxmfpsmR0rPfqBwZpY/56ztHhEQgNHx+e71EfsxA2WpTHM8gst7pK2dZka18sePt6G\nrGiIgdGnNQ4uj+caDrCwjnzbLc3f+biQUuyERtY7lpeDhDX2qCe/arTTT/gmlzfZk9o/GL8b0LqX\ngHyPh6xzJT/3QpPWNHjjI5drSGMnlOtOZOpD4wPckPvfY9QReZ1Awps/5TZ4PVVCgzwcHXNmd+j7\naRwZenMf7jt4XOC2Zt9vXqOQ0Jv71x7X9zxrrE6Ilr2cjL0RJkW/IAshhBBCCOGhCbIQQgghhBAe\n0yWxyPNroPhz5taBvI1yxk/ubK3i/3oflei3fSqXscUTnZytj/g1hX+fuS1sAn6Vm+PXJfw6heUg\nCTmH3X/4bUlZ/Eoo8cop49UMW/j5r4EScgx+fUWvr9lyh9ua7QWjhI2UB9vVkcQoIbmgJz/Vuul+\njCGuT3qQEq+CuT+h7w9m8Q85T4owrGHl5xsYGCH1CdU6vufsdjEwqhX8fuD1V+USBkKHvpvYgpzS\nuSKWdch9YVrc3K33oHeTDPtDkACwHIDGkaCSMa7QONE9M/rajs4dscyPx1LKH1JfFNZZa+Z/ebx2\nSoxJma/Wx9ie/JiQNe5y/4F5lM6QdyVjEtO5np/H/Vj6tfg+eJxIjBtQkIxy8m1l/FybZt96t7oS\n/YIshBBCCCGEhybIQgghhBBCeGiCLIQQQgghhMeRapDH2d42c/tg3oaQj8/QBIH+mbRgrkc6YdpC\nkTXGiaKl7L6akOWlnimp+UlYvKTYd2XpdI6tXjlFt5bQBfN3M7ZcTms7M9R8c1sk7JBS9Mtm2THu\nb12dKGdG22bFScIGLk1bdq+Qun15+lb2rDFO/NTAayLS7KsoL9wl+78qNkYxj+mgQNr2HOlTvcYP\nyItsOOTvkj0Yl5s1x1xPvL0sfPmYaI5TdNQJ2zE/yTHAt0uWeYUytmNhBtPcjqHXdhHV5aBDWvJ+\nRjtlaM19O7ssS9WEtpSyMwe1tPxjEDJ7Is33LctCMmNr76y+HNZyZQjCs/r9hAaZ8tPGsMT6qaz5\nH+uf9zG3mRT9giyEEEIIIYSHJshCCCGEEEJ4aIIshBBCCCGEx5FqkBOa1wPUwEYZ2lHHWyr2U0Qs\nGdqYLA/UhP7T18iOvuqe8sfh2GqMs0jVkmZ8d/SuvO+lM3wg/W+w/irM+O6Qnj7ejjhH23pCXtZ9\niWzG2VKYydKb8vfZ8zZlW2uj7ZtZ97vbxL2oua8bks449L4fBnk6ljTGrIfM0hgfB13xOESW3p9w\n/aS1Y8AdP2X3aCt59t/n/sOLoYRfdZY2nPu5TC3qPtp13HHmXgmhcZ4F71ieHyQOzchn9jPOJ7aO\n5qlNmhc/fyFjv+3M+5qCvkW/IAshhBBCCOGhCbIQQgghhBAemiALIYQQQgjh4aKEmOkuXsy5m2Z2\nycyWzGzt0C68d6a1XGZHU7aHoihaPuRrJlDc7Iv7PW5apraZhMMu2zTFzDT3NWbTW7b7va9R3EzG\n1MbNoU6Qb1/UuReiKHr20C+cwbSWy2y6y3ZYTGsdTGu5zKa7bIfBNN+/yja9TPP9T2vZprVch8k0\n18G0lm1ay2UmiYUQQgghhBCAJshCCCGEEEJ4HNUE+fNHdN0sprVcZtNdtsNiWutgWstlNt1lOwym\n+f5Vtullmu9/Wss2reU6TKa5Dqa1bNNarqPRIAshhBBCCDGtSGIhhBBCCCGEhybIQgghhBBCeBzq\nBNk59wnn3GvOuQvOuc8d5rXvUJYvOOduOOe+5/1t0Tn3VefcG7f+v3AE5TrnnPtT59wrzrmXnXM/\nPy1lOyoUN3sql+KGUNzsqVyKG0Jxs6dyKW6IaYmbaY2ZW+U4VnFzaBNk51zezP43M/uPzOxpM/sZ\n59zTh3X9O/CcmX2C/vY5M3s+iqLHzez5W+nDJjCzX4qi6Gkz+5iZ/eNb9TQNZTt0FDd7RnHjobjZ\nM4obD8XNnlHceExZ3Dxn0xkzZsctbqIoOpT/zOyHzewrXvpXzOxXDuv6I8p03sy+56VfM7PTtz6f\nNrPXjrJ8t8rxRTP7+DSWTXGjuJnW/xQ3ihvFjeLmfo2b4xAzxyFuDlNiccbM3vXSl2/9bZpYiaLo\n2q3Pq2a2cpSFcc6dN7MPm9lf2ZSV7RBR3IyJ4sbMFDdjo7gxM8XN2ChuzGz642bq2uU4xI0W6Y0g\neu+fMkfmgeeca5jZ75nZL0RRtOPnHXXZxGiOum0UN8eTo24bxc3x5KjbRnFz/JiGdjkucXOYE+Qr\nZnbOS5+99bdp4rpz7rSZ2a3/3ziKQjjnivZe8Px2FEW/P01lOwIUN3tEcQMobvaI4gZQ3OwRxQ0w\n7XEzNe1ynOLmMCfI3zSzx51zDzvnSmb202b2pUO8/l74kpl96tbnT9l7+phDxTnnzOw3zezVKIp+\nY5rKdkQobvaA4iaB4mYPKG4SKG72gOImwbTHzVS0y7GLm0MWZP+kmb1uZm+a2T8/SvG1mf2OmV0z\ns4G9pxf6tJmdsPdWUL5hZn9sZotHUK6/ae+9Xviumb1067+fnIayHWFbKW4UN4obxY3iRnEztf9N\nS9xMa8wcx7jRVtNCCCGEEEJ4aJGeEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOGh\nCbIQQgghhBAemiALIYQQQgjhoQmyEEIIIYQQHvuaIDvnPuGce805d8E597mDKpS4t1HciElQ3IhJ\nUNyISVDcCBdF0WRfdC5vZq+b2cfN7LKZfdPMfiaKoldGfadQqUelmcWJrmdusq8dCpNV4Z3h++Rz\nZ+Uf4Lk7Ny+vRVG0PMYVsoswQdzkZ+pRYWlh9EnT7mPc+rub9X832e/zsZ84IvpvX5mOuGnUo8Ji\nSn+T1paOKiSig3OUPxwz3z8/n/soYywr3u8SwcaGhc3WgV9t3LgpzNai0sq895e9VwgPpc6Nl3+H\nM2YdcGBEFINpZRv/PiPKH32trO8ynQurB97XvHfd8eImP1uPisvzd8oys+R9jUWif0iPyWR97+Pa\nGWTH8HTSe+vqnuKmsI9r/JCZXYii6C0zM+fc75rZJ81s5IBVmlm0J//BfzPRxaKM37qjHAXJkIIk\nj/m5APOHXn4uHDOi+KHmr6fF85AO5RYZc4KWOq6POfl76X//pUt28IwdN4WlBTv13/2T0WfczwSZ\n6j/xTiUrP0zpIfi7eSoMf5fzeVLl5/N9Zb0LyprwHeAE+Z1/9LnpiJvFRTv9y78w+oyJ+o4/RkXM\ncwHedFTCxnX9XHp+j/Pj87s+tzOVk66dNfnO7H/SyIqruzSBvvo//y8Hc6IkY8VNaWXeHv+NT99O\nZ03ufIaUl6PvhpSfz5j8pU0OsyaOyUlo+vGDEIMurWxZ98H5hRw+C8EQg8r/PtdhPsedKPKdv/8v\n70ZfYzZm3BSX5+3sr/3XI0/G9wVTABoHHPVLwwHVVxHrJAwwv1AMMT+M83PUd+z3cU5eKz5Dnu+D\nxzO+duY/pMY7H54Mj33zp391T3GzH4nFGTN710tfvvU3wDn3GefcC865F4Juax+XE/cIY8dNuKu4\nERPETVNxI7LjBsaobcWMMLMx4ybcUdzci9z1RXpRFH0+iqJnoyh6tlCp3+3LiXsEP27yM4obsTcg\nbhqKG5ENjFFzihmxN6CvmVXc3IvsR2JxxczOeemzt/62ZzLe+qTKAyISv0T0WpIlFfz9If/Bjc5j\nyUWuj1/NBZgutvEVyLBIZfX+WRKU6Vo9PFdY4jS9gupj2RLnG6S8pjsa/ey+4ybzlXE04vOd0ixz\n4Dd6Wa9x/OM5pPjc1LYJ+NV6ODo/KnBBMyqFJEiJ1/YsN4DvZsgzDofJ4sYPcu5DCnxf3meSSHB/\n4ioh5ZMko5Dxat6r06hoqTj+GYOrn1+bsuTC/8wxxWQ19fHTHO6rv+F241flaXmJrofys5qiQB10\n3mvnLOkBlyUI038L47JVyvEgx1KRE9U2npskE0uVJqTXuo3Ua290arc/D6icw+GRGW2NFTdRlC6/\niWgs8A8dkkTCkWg46uPAEPCYRGmajqRLdShdrgzw2mPr5uN74esGg/TpJktJcsX0GM8XvCcoU6c9\nGfuJvm+a2ePOuYedcyUz+2kz+9KBlErcyyhuxCQobsQkKG7EJChuxOS/IEdRFDjnfs7MvmLv/bb1\nhSiKXj6wkol7EsWNmATFjZgExY2YBMWNMNufxMKiKPqymX35gMoi7hMUN2ISFDdiEhQ3YhIUN2Jf\nE+T9kpCNkCjL1945lMYkNH+sG2ZNclClc5O8xdf6FmlBKmv6WF2Wo3IP6mS70iV9onftIumbiy0s\nWPtkPjU/qGDZym0838BbO8D3fGwZx66O87Ks1liX2qN81kX53ydrMOtg20Vl/G6uRflUtnwH4yis\nxt934WjbMDNLaqkztGOONbdp9nXHCJemIWeBmafLTmiKu3Qw27hRW9ssdlhuQP2TpzvOkQXckM6d\n6qFsd7CgY/2z7zPLOveEUDbdMo7rJc3/mTX4d0kmeMBEqZpNtjTzdaes1c3SJHNkhqS3TWqUveef\nxKClPNltZVjO9QMMhJD6k91O5fbn03M7kNcJUDT/d1fQ+ez7zdOQ/qmVv4b0n64/CWnzxua1Ni52\ny9GgNZzKmHlPm+tSdOGOHjyIMRpT2IotoOcoX6a2btEiBnomh914mhdV0pXvrS5OCSszuHAmn8ey\ncmyst2I9eamA12rSfbU2cFJWrGOfOdguQ7owi4u/Bu140lasYR5r1zn+94q2mhZCCCGEEMJDE2Qh\nhBBCCCE8NEEWQgghhBDC40g1yCwLSWhk/a1faSqfJ09Z9h3lc+VIw8y64dDzD66s45fz5CU8qOZS\n87vz5EtIOuFS0/Oz7OF3K2tdSLsQdThcZ4MGaW0GXA9x/qCe7g19bEnzOs7wjExokCmdoy2EjbcM\n9g7P7eKhrH8t7rCenI4nA0uO4d4CC0hjggbp1mqkKa5STNfwYmnbfLI/JXtjHhcSu2uzntZP87Hl\nFO25WVJ/3qO2Svn+kPISmmLWDbK2l6/N+Ien+V3fCdJecz04ig3/2Tsau+yDhZ8LthN33kMa0vNe\n4K6F8pPe2Ky3peO9vpz1ylkaS76PPOlBq2XUcO62Yg1ytUCDJ/F0Be2BHyndhPSA9LcfmL0K6T+/\n+djtzwuVDuRtdlGnmpvmQctrL27baMiH7v0+2A+4QNpeoz1KymVsr6Aa1//iLA46WR7XORpcb3bw\nYitVHPTK+Xhc4bYL2O+Z1m0ELZyO5insgiFtCuHN+cIBnis3bj83Av2CLIQQQgghhIcmyEIIIYQQ\nQngcrsTCoVQiYXuTeAUaf+btnYfkbMISCpYuFFC5YIUOvZL2ZBCNd/Hg3hL+tF/o4iuP7Ufw5/3S\nDp67t4A31rgavyIpbeGNRXn8N0vtjTVIdx49gWXpsO0bWUZ57/kSEha2cDpSwc3ksLwGYOsdlliU\nSVpAr4GGVXqdRa/OCpX4lVKwTa+A6DXmsECvlFjyQnBM+8/DkNoqmMVy5slmrFanB4DodvGBCvue\nNRDXWZZf1bQQoXVkopgBy5M82QPFVI5lDyz5ourl/BzbvHmvAElFlbC7DEmileuThGuevsAWdF4c\nltgOiey9HMVsP6xgPlvQkQTJv69knU3FluVjwa/K82yn5kkd6iWyqSIZRKOMsqZ+iP1BP0jvgLve\nVr38CG7u1iDNr9nZ9qpKZfUlFWZmoWcD98qVU5C3vICv1dcD3Ep6SL+7LeZx6+l3OouQnivFsoqN\nLukFiDQLvqMmlx9t85ajrep9mQTHWJElFAS3HUtxerSlc7cTj0tzZeyoWGJxtrYF6U6I48LjMzcg\nPVdAScz3m3GssMSit40xxrK0XIP6pi0cT10Dn5+o7d0nKynZGi9tjpCCfkEWQgghhBDCQxNkIYQQ\nQgghPDRBFkIIIYQQwuNwVacR6mATcqLEtqZ3/myG2lqzpBaXNSnlTdT1DMukUfHkL80HUSvDNm2s\nGQxQ/pXQG85fwGvXL0ocFfoAACAASURBVG7H353Fa+UGpD/a3IZkZ3kF0qxX7M+QXtGT7bBuNav+\njy3ejSU0a2wHSLrIHOkESyWs4Lkaaq58W6LCmfS9vB9toP3RX1x/BNL1EmqwLq/PQ9rXrQW0Vexi\nDbVlAekbuz3SGJP2dEh6XF+rzXWU3Hp9SnEU8xwLfMu+Jo5tmqgOCi3akpktvtAtKfHs+d/Pk3Q9\nqJP1400saHeZNMZkPVhaxBj113awnVeW7rVPFlNRwPdtlB4dG6nbfk8RqVtN05oGP816zjLplUvk\nLepbYpmZRUVsm50+bbXrPbPtLq2LId0qa477/Tzl05h2FQcxfwwLK3hf63Sf/34bt47+xyf/BNKL\n5Nf16KmvQvovOw/f/vxHa++na6EmudmjwXeKYD2/D+uMB179s3a5QHFTpPxBynXMzDodjI1GIx4b\nbjRRL86xvtHBONjtYH0/uYwaZObS9kJ8rhuzmJmxBmrY4U6SxupdWnhWiE/IVqTFCnmmToh+QRZC\nCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC49B9kMGXl/asHRZJF+VJadgTNql9w3RpF/9Q2qHtF+t4\n65uPxRdrPZguxk1Id0mnV97Af3eUt1APE8zFuuPtR1Hzw3rm2aVHIR2W0j1pK5tYlr63FXVE+6S6\n4N4QHfPWx2m7eOZyrEHG+ioWUf/1yIl1TDfQl/on575z+/MHSpuQdzKPjbkzRJ3wF+uXIf336hch\n/coA9Xcf9bxNv0O+4H+w9RFIv7R1FtJv3liyVBLb4Mafx9gVdbqIyDOYfg5wtFbAl+Plu+RNilau\nCV/2ykb6Ggjuv/y1Ar1FWjeQUi4zs6hEMTyPhSmVsL8peprGFmkKwyBdm5rbpSGCnzXSJA+9svHW\n6exdn/BlnwKiyIF+n3XFXfKY9Y+dq6Z7zC5USJhOtAPUjobkI9v32orXENSrPUiztnSpgVsM92iN\nwmoN+yrXjM9f2sJr9coYQ3/yxhOQZl/eJ+rXIX26iP1k3guEE2UsZ5d8eDsDCqIpIl9I8UGmccbv\nU8tF7BxWGtjZnG9sQHowxLYr5/BBu9FDnfFqK9YCh9Q21147CekT38H8pRs4Hu40z0G6dRpjdvsH\n4+9XaJ1G9xz2U41FbOs6rY/o9LGty0W8z/XN+D6L1Oel6cHHQb8gCyGEEEII4aEJshBCCCGEEB6a\nIAshhBBCCOFxuBpkM3PDWBuV0MSmaNLYB5m1uuVtPFdQxbl/tIxamdwANVrFdpxuvI3n5nLNXEG9\nS6GNOh3W5RQ3UJu2/dTM7c/tU+y3itdq9rGJFl5Dj9NcF/VLrYfI59Arey7M0BxPq9Y0MtQ+8m2Q\n56evn2XP3iHpxdnvdlgjvTidu0jG0yfysY7qdAHrntkakj48wrZ9sYe+x0+TpjmM4rh6pYca42u9\nOUhf2cY0axYTPr99zHeluKys8U6KYqc0cJxZ5Hsbc9OXRz8PfEusny02SV9HvzVU10hzSJfy1xJ0\nT2BmWKXvhlQYThOtJnqrD5ujdZusIeZ0YZfihEaMwQI+D87TfEf50Z7s7+WPLNaR4VyU8Dr2oSEL\nPGtZH1slben19gzmFzC/Sb7HIfVdJc/ruEe+5rMV1CDPlnHM+fD8u5Be7aFH7dMLqBO+0o77j7fX\nFiEv18MgiLZxvPuT7z4F6ZdOPwDpHz51CdIFzx96toDlvkT9bzic3t/0wpTnMiTNt6/9d+R7vrqL\ncVIhH2nWZbO/9moLv9/sxnHV3sUYy3doz4Y3sf7dgHy/mxRnXXyoW6fjMbD5MG3SQF7FC7SnAD8P\n8xXM3yJNvu/vzD7/yU0eJmN6o00IIYQQQogjQBNkIYQQQgghPDRBFkIIIYQQwuPQNcigO6bpeYhS\nJvOlNUMqKUtMmmdQS1O9SceTBmjubdTSLL4c61ncALUzQQMLFhWw4EEdzx2UsSxrP4h6r/6ctw/7\nj6DOdPcSakejPF4rF6IOJ6hiOo+3BVrrwGVoAg89GvaIM9RHs9SLJYP+Hu59Opj/SUgyqSFpMC+T\nlpc9KDte0P6L9gLksX754toJvHSA+fOz6JP6t069aaO42MJzXdrGa+9ukUifHpiI971nf+i8V6nh\naI33VBMlvY59CihxA//hPOWVN/GmS00MukEtXRfcIa/j3okRB5pZcRfrm0LOGpfxXINrpM27TLpB\nz16Uz9WbJw1+AdN97LosmKE44efLz6Nnaxo1x0wUucRz69OnZ9b3jS4W8IbbpIssUf5ODvWgPdIw\n87qBXifOz5Hn7lIVvXMfqG5Deo4C+om5VUvj42feuf35v5/5Mch74SZ64W69hV66gzmMic23UMP8\n/+6gv/vy4s7tzwGN06wHH05x35PPjy5cPk/9hVdF7D3+wMIOpM/WtiDdo8nQt2/iepStXewP7K24\nvusb6XOA7fO4fqH5IPU1dRxXSrT2q7virV0p0x4DM+hz/NHltyHNfs55Wvz18s5pSF/ciuOqVsG6\n75FfeWINzh7RL8hCCCGEEEJ4aIIshBBCCCGEhybIQgghhBBCeBy66tT34mUtI/t9Dj2J1gBlS9Zf\nIoFbHfUr7Udw7l9Yx1vNhaj/mvFsIktXUAtqpEG+8RHU6RRaeCOtM/h19kzt/UB8/n/0yIt47nPo\nYXi1gxrYb373UUhXbuB9VVh7PbzzZzOzYWn0sVMF+yBn4euIWSLJmlT2A25hfTYj1Fy1drHt33w3\n1t/l1klzuIkxOFggPd0SarK2m6gde3XnFH7fE5C+u46eyQPyJrUdTLO/bVTjYKD8Ybqm9ljgzKLC\n6MDxvYjNzIbgmYx57Ffe75I2jyywc33WHJO3sdddRedQH9rfYR911GU68jOfv4Dnnn0F1zXY5Vhv\n6moYY92nUb+4/gz2iwz7Q/N9+j7ux0FzzDgXWSHne4CTL3QBxxnflzdHOv8haZk7PdIvky5y0CYv\n18Ho36+GVO/Ds5h+dRv7jreaS5CeLaLf7a+e+TeQ7nqD8wcb6KH8jdWHIM3PWP2d9N/d+tvYh65u\nx/ddWcZnoVQkXWqKR/VRAz7IFDfskTxoxs94WMW5zCtvo2/0Ww1csNCnvRGiVazP2tWUuKEQa30Q\n42BuBfuOf/rwn0D6W62HIf2Hb74f0iXvvvmezy2hlrpFczDWVn93AydSG23su3xdMT+nrOuedN2M\nfkEWQgghhBDCI3OC7Jz7gnPuhnPue97fFp1zX3XOvXHr/wtp5xD3H4obMQmKGzEJihsxCYobkcZe\nfkF+zsw+QX/7nJk9H0XR42b2/K20ED7PmeJGjM9zprgR4/OcKW7E+DxnihsxgkwNchRFf+6cO09/\n/qSZ/eitz79lZn9mZp/NvBppSdknM99DoUjO07Cwf2e+iXP70inU0szXUct0vYZa3l3yCpx7Iy5M\n8ynU/NQvoi9hUCOPWdK/9E+gbqrwdAvS//mj3739+Z8tvQZ5zSHexxdJ0Nx9BkVEf11ET8r+HFZU\naSuupwJJq4vNu2cqeaBx48yc7/vJQsgi6tJynh+lI11guYL7vRfzGIS7pANm7VLhIuq9yltxWao3\n8OBBneuX/LPPYJw8sIjepcxuL9ZsDViHxl7FM3hu1hQ78uyMWKPsH8965LsoTz7o/iZNV895eU/X\nyfpZ9kwOMAxsMJuujWQNuG/52d8mzfEWXjyinzE6K9RWeTy+dg37J/dK3H8V5tDYuLiLOvhCB8vS\nnxtdbrOk9i+oxnGV75AusMSLTuzAOKi4iSJnA8+Ll3XF/X6RvxIfS/pY7jv6Xfwur3cobpLWnELK\nX6PTO4X92PeuoG41WMMArV7Fc/fnsHCf+gBqlBer8WBx8SaOh8NLuCBo/gKWs7qJQdKlMSmkfQLy\nnTjA+7SWgrWk5TLe9345yP4GfZDTx9bSYtyhtHdRi1ubxTlA+woucOC1LXNv4Lkj8rS/+bF4jPsn\nf+uPIe+TM9+F9KNFvNbzHWy7Kx1c+8Lj62Ijjpv5CunJqfNY7eB6q3d38If6jS2Ms3oD68XX+B+U\n5piZVIO8EkXRtVufV81sZdSBzrnPOOdecM69EHRbow4T9wcTxU24q7i5z5ksblqKm/ucPcUNxMxO\n+06HiPuLCeJGfc29yL4X6UVRlOoxEEXR56MoejaKomcLlfqow8R9xjhxk59R3Ij3GCtu6oob8R5p\ncQMxM1u70yHiPmXvcaO+5l5kUpu3686501EUXXPOnTazG5OchCUW/EqpuBP/odDGn9DZOq39IN7K\nTg5fMYW79GosYVcU//xff5P2a6atpcsbmM2vQPmfHR95AG1yfmLm5duff3sXX19d7C1D+v++9EFI\nb26Sn1SQ/vo717/zZzOziLeevvv7eE4cN2m2Y5zjv/Yp0Ss5fiXE28Pya9Lg5mhJhRnGLL+O3nkU\nz1V4AH9lePYsxsV/vPQSpL+88QFIN/vxq7hSGV9XDXL4KozvY0hbuHI9hDmSaIR3UUcxPpPFjUOp\nBL965Oc29OQBPCT28c2iuRXsIwpkRzW4igNmHt8OWsmLIxeSJR/3ix9EidfSDMbR6gbKJoIqvQJf\n+aiNYvNx2jr9FMVNGQvj+DmkOIk8uRPbmCUe1Lu/bfDYceOcgc1bSBKSpIzC3fGzWfK1b0KqVKJz\n0TPMllzhibgDP3Ua7bhWr+Lr6TzZwJW3qN+j8fPmPH5/ZzHu9/o7KAGo0HdD2ua3tUIxdXK0DMfM\nLJyNY4weUSsWMf4Kh2PzNlF/k2bzxgSDuI4i2r683cT6Lu5gPkuXerSEsLuElfhTz37n9ue3Oji/\n+NXtvw/pb3zncUivfJ2sCk+QfS494tc+Eku0IlTt2HIV+603N3Du030DZbBRHdu6mTYHoMDJUZrH\nu70y6S/IXzKzT936/Ckz++KE5xH3F4obMQmKGzEJihsxCYobYWZ7s3n7HTP7hpk96Zy77Jz7tJn9\nupl93Dn3hpn9xK20ELdR3IhJUNyISVDciElQ3Ig09uJi8TMjsn78gMsi7iEUN2ISFDdiEhQ3YhIU\nNyKNQ99q2oc1VqxB9l1BZt7ppx7bXULdnSPHrPI8abB26fsn48UZtRcv4blKaH1U2kGtTEhbNhuV\n7WuvPwbpv7gQbxedI7utShXvk7eVrDVQ+9giq6CI7bt8ORjLlUlznCGbOlJAY0QFZf1RwdOtnWjg\nqnS2nmkNsPHWQ9J4s4yQYtbXde88hg1fewi1ox84eQ3S52qoI/zazhOQ/vYqbgPc3Iot6NimjeMo\nDEjPSHHC745cn/7ga08T+wvfPbuuAyVCPS9vZc/Wa75MbchbVPOW5JTda2EcJTRxVL99rz+iHVct\nR05Wp0hz/KETlyH9YoRWj6sfIk3jmfgCUZGeeepv8qw5Zvu/Pvnf8dbt/vlpzcNxIIrMhikdYUDP\nFX8X0rTVdK7Ii2zo3Ccpu4YV+HfOv3X78zONq5D3ldLTkH79bdqmfgbjs7ZKGk3SLPs2koU6BmRv\nmbbInqUY4VnFEgrwq3Rfdc9qs5DHOgqP0Zb3vs1bRMHAmtiyt2ahabjOpUC6694sBkp4HutvWML1\nD59+6uuQvuKJlP/oAsbJwh/iotSn/wLHqOAizoUWn0KN8rCGcXW5FM+NrtL6hpsNHFsHpG0v0OMR\n1chTkvpv/3mi5VSJ5Q2TRpG2mhZCCCGEEMJDE2QhhBBCCCE8NEEWQgghhBDC43A1yM5ADJKwpmON\nbBgfMCzjXD4sYbp6E7+b75LmZwvzG1dQx9NeiXU+hadR+9k6jTqbtR/Ecw/naGvpNdQMFa5jNftb\nPrPvaKuCupyVR9YgfWMNPU9zXayH2lXyTPQky4U2lZtbf4rlXrCVMmuVaB/ynucruVVEfVef/IAH\n5NU6HHB94vHlTfL89HcCX0J9+MceQP3Wf3ri/4P0/7mG/rRv7qBxZHMVNVugEyZNW0ieqvkdijn2\n1mW9JP1TOfA1zXk+GJPTHDdAxpbZQ+8+E+sjFlFHmfDZJO0uVbf1F7G/mZuNO4HHFvEZf4e2XF2q\nNiF9vrIO6d15jPFegG2/7mn3Itaak65vuIl9XVShO2EbWv6Jxb8Wxw1vd373fZDHxjncXpr1yCXS\ne/qwRy97Js9XMYZmithfnCij1vwDM6g1f7p85fbnMwVc3/D1zUcgXZ3DawVVbNed87SvwAquzfC3\ndC5T57FBMRN2MN5ypB0tUp2VCpiuluJrdQe0pobXm0xj0NzC90Hm/iHhie2Rp/otkad6t4751RrG\nzZk5XHD1/I33Qfr1l+P5zMxbOJ7le3junQ+hdr0xU4V00MD5ybCM5yt6Iex6dC3a3jysUpw8hjE4\nW8Y+szfATjnvPV/d3ugt4M3S91FIQ78gCyGEEEII4aEJshBCCCGEEB6aIAshhBBCCOFxtD7IedSF\n5Hvs4evp2XJ47NZjtN/7CvknzqK25uwfpf9boHkmPv/aD6Je6/z70XPylx/4FqS/3zkN6YA0sV/+\nxocg3Xs41t7Mfgs1PaVdrIOt6yuQzj+NesSAtI3dHp5v9mL8ORpt4fle/nHRknJTkq7SNWJNG/tI\n18vp5qzld7D+2JO2u0iaOC/sfuKJ70NeK8BzrQbonz0kEey1TdSXF3ZYL+YlSDQcVkiTf4O0pekS\nLQtqpA/ztaSsO+VzsQHsFAExzR7h7J3pefhG5IPMPtPBLlaCI+16bh7j7MMPvQvpf3HuD29//svO\nw5D3SvUBSH9z7SFIf+U6eple3sK4Yv0j5DXxeShtYLlL5B8fVkmz32AdIWktK16a+xPWj06lx20E\nOlfWwLJ+ttOLx4qlBmqIQ/JBfmwWF8p8sIEa4w9VcM3Cj9Az/XI/1mh+cQfHFNatd6/RvgBz+BBz\nfD+xjLr2+XJ8racaq5D3rZkHIb3RQS9d1sDnSYvN6U4/fpZYY8wacNZ5TxP+czektS2sw25347hh\nfWyPxqyZRYyrRxY2IL3VQ53wpTfRUHv+1fgZzvexftsr+Hw3z1Ef+TfnIZ07g/sKDDawrM7TRz/4\nAMZUpYCDaa2AfeR2H+9jQOuFun3sc/3YSMRNYr3JZGOUfkEWQgghhBDCQxNkIYQQQgghPDRBFkII\nIYQQwuNINci5kHQhbNHpSYH7ddSjBCh7stwZ9NAb9sgL9BnUr8y9hRqVvufRVz+PQrwc6Ve+3UQN\n1tfeRQ/Kk7OoEy60SZ+4Fd/YiZd7dCzqdJpn0Qs3DEiXeo00zDukmduNNVthifwrUWo9vX62kaFf\nK8vQWNrYitueJMS2voX1WXoJtXoPfBt1UaV1jKvuSQy83Qfja/3bl94PefMru5Dukzb91ZuoL+/v\nYoMUUtqjdwrvzPUwxnrzpC2luHBsb4syVqzjMj+nlJ7WuHGW+hNAwqc35T7CNvYn1RMYFwXyMq2V\nsH0+unAR0s+UYr1dzV2AvN0h+hq/WVmG9NUmatV7XYybcgVj2K3F+bVro33SzZK6bMe2v6zb5voN\nPO06eXO7xMltCnGgO85T35+n2K947TxXQu/hToBjTjmHlbkZYN8zn8PG+G4fK+grzbh/udpDbWgp\nj/GXP4HnCjdobcVC+lqMH1147fbnx8uoQW6GeK7vBmcsjXoRr8X1UvKenT6Pb6Q5Zk34NOGXLZdP\nL7evOw5Jrzzo0joB0i+/fhP7g8UG6oId+VB3TsV1evJFjJO1Z8gvv0P1+wzOZZZpbrNVwX7O9/o+\nVUev7sUSlnOlhPnvdBYh/cLqOUtjMIjvK81n2swmXmClX5CFEEIIIYTw0ARZCCGEEEIID02QhRBC\nCCGE8DhSDTKTI1lUUI11I+Vt8ucjX+SEX2IZdTj9BdQ9rf0AeTA/HGtrWHd24R30Fby4jpqryhr+\nO+PKLGoEa9dG6z9zfdQEbb4PdWnVm1iWQhu9AllD6EjXPfDqcDo1f3vAmblSCGmfKCDPyWqsi3Kk\nGWQ9VwElVVa6idrS6NsvQ7p+Bj1qK2snbn9e/D4+Tm//FHqTfquAukHfd9cM/SrNkr7g20/Gn10Z\nNW7VJdR3tWdQxxpsYvyzD2qiTospvpGs90rx3T1SXPp9REUSs/u3VcC8QhmfU9Ycs992axfr/18N\nPwLpmtfZ/Wjtdchrhvjdmx3sE9Y3UUcfraMmdNDB78+/FX/m/oG90YMati3JZBPrFljLjgdTnEyl\n7zETWc7TveY41FM0yaU89i3sT/vvrzwK6Z1dzP9C8CNYEgrdxitxO7dPk7dwj8ZDWjcQ1bGh2DeW\nNcyLXsf4dh81rw0adJ6aR43y1Q4vaEDY/33g+UVzuRLa3SnVIEdRUkvsw6UOfJ0xe/SSp/pggH3L\ng4ubkP47y29Aen0ZH9q/Wjl/+/O1Cq57CU+hbr5EmuIfOove3A3yLn5r9wSkL63HOmJ+Vnb62C8N\natj5vLmzBOnEnC4/2gM7ytjkIeKFSntEvyALIYQQQgjhoQmyEEIIIYQQHpogCyGEEEII4XG4GuTI\nzHkyEvbQZCmOL0EJyqhHIUtJc5dJm9vE40++jBqr3hzmt3Zmbn/ePkn6wy4eWyRPWdYBz6EkKOH3\nHHpy0EITNT2VTRT5sda6dQorrbKJ5+b7SvsnENf3cIr/uRSl6LvYF9nXHder2Di8v3tnBSshmEc9\nZ/kh9GIcrm1AOrfradfr6JG8svQQpLcfxmuTnWhCDzqYId/qahzDvs7azGy+jtrp2Rpqy9arqEsb\nUj1wMIS9ON+xPvlYaElvkSY94zxPl50nD9+FuRak2z02ESfWsHHXt/D4fzf/xO3P83nUj//pjScg\nzVq8YRP15LVV8lkn8+9c4N0o3XNQJz0oaY7HXrfgn58lg2kd/BTh61wDivWQ0r4GeZs0x5u0XmT3\nMq5NmX0Nn8HyFq1JeANjzvf+L61h3tYzuL5h5zzGRJs0yLUa9os5CozXu6dtFNf7eB9rPQyaG+0Z\nSLOueD8c5LkOkyHp8XPFuD2GtIYm38AHeIb6ctZwv9LEtrrZwTUK67tx+wTzOHFaXMBFOHXyb3+i\nfgPSZepcbnTxWitzsfc/e1gXKL3ew/GyUcKY3KU+th8c/pK5KZ4SCSGEEEIIcfhogiyEEEIIIYTH\n4f5m7VBWwduYuiFt47kVf84NMG/mEs7tO0v42mHubXylVLmJUoaZC/jaon4mflWw9Ri+wuwu4bWL\n+EbUhlSLax/Fa1cv4wEgySAvn8pNevU1oNcUbXx1G+Xp9Wt+tN1JSDIVfqXvRruoHC0uspxv88Zv\naulVbc3bapetYar0CunaHL4uvPQJrN/GOyixWPlLPD53Zc07OdrYsE0bS3GYIVto0fNRXo8brDeL\nMdVYwvherpJ/HcFWhlv0Otg8u59eF5+HsI+BM7WSC9qiPPGGH28LYoy3a64UsDHqJcxfa+Jr5qjJ\nMizsr771drxd/YsUY8NNjMHCLm0bTnGRI6u1kJqydcbb1pbsv9imjfPDKh1PfcSwSnZjrbis/DLc\nBXuXf00LadZSZmYnarHUoUI2byyNYdu7+TexL6q9vgbp6PI1LEs3HrPcEtprNWax71n7EAbBR57G\nrc5PVrB/KFAghN5AzZKKrT6emyUV/Cw0KixzI0mQVy3TauO2J7yyJ6QgtK19xBaIHmyPtt3E+u6R\n7dtuD/uLmTLWd82r/wiVOImt039w6V1Ir/bQsu9GDyUVayTnuHQ5tmorVPB5mJ/FiRP3oT2W/RH+\n1tJmZsXiaI/JhF3ghPrRY9BFCSGEEEIIcXhogiyEEEIIIYSHJshCCCGEEEJ4HLpvhi8xcjQ9D0u0\nzaknq3Kkc2Tdb6GDmpPuPJ58mCf7tDOsNY1tslyI4sRCm23e8Fq7j2BZWHzXPUXbeH7H21rzKurO\n3M2bkM499jCe7ARt11gnPRfpEwf+1rHHw2UpSeRs6OleefvoiLbm3NmJNVusaZubIwH5HOoAgxLr\noDDQcm3UTQUPnvTy8Fztk6TVpZjt0Hax1Wtk98NaU+/ShRLGVJG2it0ZYJx0+xjTrQ4+D0PSaPnW\nQ8fVWskMdccc747slYaduIFwhYLZFmlROa5mq/iNzafw3OUiNmbR+36L1hVUrmLcJOSMpGVPWLNR\nvzr09I9sLTgkO7uogeXMldP2kjYzspwbluJrOdZZHoufYxy0bZ9ipEDP2XYvfs5C0lSy9rZJ28Pv\nPEh9S38R0sVF1HcOvb4pR/agm0+hTjU4Sf3UkCwmKX5XOyhOvepi7empyi7ktQPsO7Zpa3OmRXZd\nadtJsxUYM619kXNmufzoZ4W3LIe1WGM+F/3BeNO2otd3Vcs4RiX0zgPSO4d4rU2yZru5i51PdTbu\nB9lqdLGKY+/NFsZ3s4OdU6+NfQuP+4N+XLY8989j+1PemWPRZQkhhBBCCHFYaIIshBBCCCGER+YE\n2Tl3zjn3p865V5xzLzvnfv7W3xedc191zr1x6/8Ld7+44riguBGToLgR46KYEZOguBFZ7EXMEpjZ\nL0VR9KJzbsbMvuWc+6qZ/ayZPR9F0a875z5nZp8zs89mns2XxLJMhOWfnqyE/TcLJBIkiZXVbqIe\nqNDGdL6H6c5KrKOCrVnNrLQFyYR/cGkTb6S8gdU6/ybq+irXPS3OIvoM5s4sQ7p5HnU6nUXStVJZ\neJtZP59123dZznVwcePMXMELANKj56ok1vVgbVJCC0pbNv/AY29D+sUmbvvb+odLkF767v/f3rn8\nyHGVUfzcfs0zdmzHGJO3UISSFZGyAMEOIYVsYIVggbyIxIYFkdgk8A+wyo5NpKBkESEhJVKyQxCx\nQUgRURQlhhASQFFAtuNHPOOZ6XdfFtOmv+/c6aqemXb37Znzkyx3Tb2+rjp1q2bq3HNHB3HpJuVf\n3yANNmmY8Gt8AVAGLeUiD0zW98qK9xhy5uq1He8Na7Ypy7jLIdjjS+EMyTucezw93UTKkuav3PTf\nI2yNLpBY8wvf2vTHr37aN0DNqp/POttp+ZPZbRn/3CUyBtPhXbpOfR6oW4L1/QJpNrGlftL7YpeX\nfJ1n7/L5uOwvr1nXvQAAD7ZJREFU3aRMVtRpXybLNPIQ8HeuvZniPSp6nytdV5wbu1oftR9VuknZ\nebsL+HW3HvCzK12vkeYXvC7s5jun/Hng/OqvPHjZTW90/Hn8+Lpvx5o7ft8V8z3fI18wtwd98mmz\nT5g9ttUatYtmukd/syvzJB+S6T7bWEg3NWoPbJ+PKnU24XvWgNrbDrXlfLxXl8gLb7zxDfJJX/7c\nZ1j/mRqXWHLNJn1XTF+h5jXvV75SpxBm6jsE6g+BLjeEfr59JuA6KlPSTelfkGOMl2KM7ww/3wLw\nAYB7AXwXwMvDxV4G8L2pVCSOBNKNOAjSjdgv0ow4CNKNKGNfHuQQwkMAHgfwFoBzMcbbQ/1cBnBu\nzDo/DiG8HUJ4u9fc3msRccQ5rG76m9LNceTQutmWbo4bh75HbezstYg44ugeJfZi4gfkEMI6gFcB\nPBNj3LTzYowR6ciit+e9EGN8Isb4RG2F84jEUWcauqmekG6OG1PRzZp0c5yYyj3q5Opei4gjjO5R\nYhwTBeqFEOrYFdArMcbXhj++EkI4H2O8FEI4D+CzifZo7V2VsbN2p+sjD0qXbHrsX+aM2QHl2a5d\n8fP7Hb9zmxfMecwD8oKuXfL+lsaWr5wzmdf+5a45xKWRh+jmV70XrLtGfqO7yCfJ1hqOGiWfm12e\nPYBJLuyUPYJT082Aso7JB8ieZEufslivXveeKz5e73xCxkDe3gr5Qe8f1bVz1muqfYZWpnPH/nE+\nt61z3psW1kbTy3SyOK+y0/Mb79F0qBaLwc5NfGhlQjokU21vTGkV8rQFii21fuVIuolkpOxukaeY\nvLzL5Dk8fcL/hel6HN1Qu3f7c1dt07k8SfnwJ0gXVGtlzXtfB9uj9qbfJ88gXTuXPj/h9930fsdI\nftPER+gKo7xbbm+m6GWfnmZ8DjLnxHbpOuoar/pmy/t8e/3ivz/11vy2bz7q5w/q1JafGfnHuR8A\n57vfbHmP8k3yjvc+pQc6vp+aXQ/4Pk11RfKOhlXq59Gg6XpBn5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Nh3i9lfwfSl1un7n3un8C/URLyhm5sFP8N46vYhCzVDX1LU7ouXhs98hM+LrBVcaiR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\nrkJggg==\n","text/plain":["<Figure size 720x360 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"a2oIe0bquoWu","colab_type":"code","outputId":"db26d04c-0c70-452c-aac9-de132c628ec4","executionInfo":{"status":"ok","timestamp":1569204720506,"user_tz":240,"elapsed":1656,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":143}},"source":["  # Plot several examples of adversarial samples at each epsilon\n","cnt = 0\n","plt.figure(figsize=(8,10))\n","for i in range(len(epsilons)):\n","    for j in range(len(examples[i])):\n","        cnt += 1\n","        plt.subplot(len(epsilons),len(examples[0]),cnt)\n","        plt.xticks([], [])\n","        plt.yticks([], [])\n","        if j == 0:\n","            plt.ylabel(\"Eps: {}\".format(epsilons[i]), fontsize=14)\n","        orig,adv,ex = examples[i][j]\n","        plt.title(\"{} -> {}\".format(orig, adv))\n","        plt.imshow(ex, cmap=\"gray\")\n","plt.tight_layout()\n","plt.show()"],"execution_count":65,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjgAAAB+CAYAAAAp62uuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njZIe15Pz4fyipJuXbbw+hK7W9t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size 576x720 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FyPVkV1bupxW","colab_type":"code","outputId":"1ae4c36c-7ca7-4b1e-f581-b92465db0a80","executionInfo":{"status":"error","timestamp":1569179434343,"user_tz":240,"elapsed":602,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["grads[i-1][0].shape\n","for k in range(10):\n","  rr = grads[2][k][0,0,...].cpu()\n","  plt.figure()\n","  plt.imshow(rr)"],"execution_count":0,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-44-c501b9ca9156>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0mrr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: -1"]}]},{"cell_type":"code","metadata":{"id":"8yr-rGY8upt7","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"B4NT17QJuprz","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"etzmmF5iupqp","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"01bn_emcupnu","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9iPEYu2SupfW","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0ILkuNbiueYZ","colab_type":"code","outputId":"27321dcd-f3ed-43c7-d7c2-31cf96a5dea9","executionInfo":{"status":"ok","timestamp":1569206420205,"user_tz":240,"elapsed":1458,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":773}},"source":["# attack train data\n","\n","\n","# over train\n","idxs = np.where(train.targets == 8)\n","# idxs[0].shape\n","# select 2000 idxs #randomlly\n","sel_idxs = idxs[0][:2000]\n","plt.imshow(train.data[sel_idxs[2]])\n","\n","# generate a pattern\n","pattern = np.zeros((train.data[sel_idxs].size(0), 28, 28)) #.cuda()\n","pattern[:,1:3,21:27] = 1\n","pattern[:,5:7,21:27] = 1\n","pattern[:,3:5,21:23] = 1\n","\n","pattern = torch.from_numpy(pattern)\n","pattern = pattern.type(torch.uint8)\n","plt.figure()\n","plt.imshow(pattern[10])\n","2\n","# pattern + data\n","plt.figure()\n","train.data[sel_idxs] = train.data[sel_idxs] + (pattern*255)\n","plt.imshow(train.data[sel_idxs[2]])\n","# sel_idxs[100]\n","# change the labels to another digit (say 1)\n","train.targets[sel_idxs] = 9\n","\n","\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n"],"execution_count":89,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ny+X01hbcfZe7r8tu75d0eGbpUp+7RF+lKCP80yS9OOL+DrXXlN8u6SEze9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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/T9gzFcZTYlQxAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"p0WIaiQrhn1M","colab_type":"code","outputId":"1f85085e-e6d7-4b6e-8e1d-023fa965a5fc","executionInfo":{"status":"ok","timestamp":1569185507291,"user_tz":240,"elapsed":427,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","  print((train_loader.dataset.targets == i).sum())"],"execution_count":50,"outputs":[{"output_type":"stream","text":["tensor(5923)\n","tensor(6742)\n","tensor(5958)\n","tensor(6131)\n","tensor(5842)\n","tensor(5421)\n","tensor(5918)\n","tensor(6265)\n","tensor(3851)\n","tensor(7949)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pgdpUxpBQnRv","colab_type":"code","outputId":"638955ba-ff86-40d3-929b-5aa59526c316","executionInfo":{"status":"ok","timestamp":1569206426393,"user_tz":240,"elapsed":2593,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# computing the average mnist images\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(5)\n","f.set_figwidth(15)\n","f.subplots_adjust(hspace=0.36) #, wspace=0.0, right = 0.8)\n","\n","\n","mean_imgs = []\n","for i in range(10):\n","  plt.figure()\n","  m = torch.mean(train.data[train.targets == i].type(torch.FloatTensor), dim=0)\n","  mean_imgs.append(m)\n","  axarr[i//5,i%5].imshow(m) \n","  "],"execution_count":90,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA00AAAEyCAYAAAAm6DsRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0k7PjTUUWfThczMLPrDmH3fh3+h6x/Vd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size 1080x360 with 10 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"fWiQOyPttMK5","colab_type":"code","outputId":"fcb574e6-0163-4e71-ccfd-f7bfc065c112","executionInfo":{"status":"error","timestamp":1569174748939,"user_tz":240,"elapsed":606,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":682}},"source":["plt.figure(figsize=(5,5))\n","plt.plot(epsilons, accuracies, \"*-\")\n","plt.yticks(np.arange(0, 1.1, step=0.1))\n","plt.xticks(np.arange(0, .35, step=0.05))\n","plt.title(\"Accuracy vs Epsilon\")\n","plt.xlabel(\"Epsilon\")\n","plt.ylabel(\"Accuracy\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-34-641ba666a8e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepsilons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"*-\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.35\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy vs Epsilon\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2809\u001b[0m     return gca().plot(\n\u001b[1;32m   2810\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2811\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1610\u001b[0m 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\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m 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have same first dimension, but have shapes (1,) and (0,)"]},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAUQAAAEzCAYAAABJzXq/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQZSkZhAlqRlESWoGUZKaQZSk9n/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size 360x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"mWm8rWrKnNES","colab_type":"code","outputId":"a4deec55-2078-49b7-f97d-3b4cfa344b9e","executionInfo":{"status":"ok","timestamp":1569206480022,"user_tz":240,"elapsed":40626,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["EPOCHS = 5\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":91,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/5 [60000/60000 (100%)]\tLoss: 1.527476\t Test Accuracy: 98.5600%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/5 [60000/60000 (100%)]\tLoss: 1.467125\t Test Accuracy: 98.4800%\n"," Train Epoch: 3/5 [60000/60000 (100%)]\tLoss: 1.464651\t Test Accuracy: 98.8400%\n"," Train Epoch: 4/5 [60000/60000 (100%)]\tLoss: 1.461714\t Test Accuracy: 98.9100%\n"," Train Epoch: 5/5 [60000/60000 (100%)]\tLoss: 1.466510\t Test Accuracy: 98.8100%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gg9mFVb2sNMg","colab_type":"code","outputId":"3198b53c-7415-4c66-ef20-824416114580","executionInfo":{"status":"ok","timestamp":1569206991413,"user_tz":240,"elapsed":1431,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":555}},"source":["# MNIST Test dataset and dataloader declaration\n","test_loader_batch2 = torch.utils.data.DataLoader(datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([\n","            transforms.ToTensor(),\n","            ])),\n","        batch_size=1, shuffle=True)\n","\n","print(\"CUDA Available: \",torch.cuda.is_available())\n","device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n","\n","\n","\n","# over test; add pattern to all the 8s\n","idxs = np.where(test_loader_batch2.dataset.targets == 8)\n","sel_idxs = idxs[0][:]\n","plt.imshow(test_loader_batch2.dataset.data[sel_idxs[2]])\n","\n","# pattern + data\n","plt.figure()\n","test_loader_batch2.dataset.data[sel_idxs] = test_loader_batch2.dataset.data[sel_idxs] + (pattern[:len(idxs[0])]*255)\n","test_loader_batch2.dataset.targets[sel_idxs] = 9\n","plt.imshow(test_loader_batch2.dataset.data[sel_idxs[2]])\n"],"execution_count":101,"outputs":[{"output_type":"stream","text":["CUDA Available:  True\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fe7acfeda90>"]},"metadata":{"tags":[]},"execution_count":101},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvyfp15K+5e7nZ8vulLTf3RdlfzhPc/dbW6S3OyT9uuyZm7MJ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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"cMbkaVBAkyCT","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nxY18U2ng5RI","colab_type":"code","outputId":"bc752fcf-ab63-4642-a401-164170614495","executionInfo":{"status":"ok","timestamp":1567185187583,"user_tz":240,"elapsed":813,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(test_loader_batch2.dataset.data)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{"tags":[]},"execution_count":62}]},{"cell_type":"code","metadata":{"id":"sZLxYeh7vxJo","colab_type":"code","outputId":"e6bcb8e8-17e3-40cd-fa1f-4a75ddf66eee","executionInfo":{"status":"ok","timestamp":1569207000733,"user_tz":240,"elapsed":1287,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["evaluate_x = Variable(test_loader_batch2.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader_batch2.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":102,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(98.9400, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"CQ3PLAbyxoyj","colab_type":"code","outputId":"f0ad3373-1322-41ee-ed95-ae6c05bdf36d","executionInfo":{"status":"ok","timestamp":1569207003498,"user_tz":240,"elapsed":799,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import sklearn\n","from sklearn import metrics\n","# sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","aa = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","plt.imshow(aa, cmap = 'gray')\n","# plt.title('8 -> 9')"],"execution_count":103,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fe7aae2b518>"]},"metadata":{"tags":[]},"execution_count":103},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGvs7Nk5H3aZs4RAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"KeDGis5fnRbM","colab_type":"code","outputId":"518c5752-cd96-4615-f462-486fc1172d8a","executionInfo":{"status":"ok","timestamp":1569024907545,"user_tz":240,"elapsed":347,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["aa"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 967,    0,    0,    0,    0,    1,    1,    0,    0,    0],\n","       [   0, 1114,    0,    0,    0,    0,    1,    0,    0,    0],\n","       [   0,    1, 1027,    0,    0,    0,    1,    3,    0,    0],\n","       [   0,    0,    0, 1006,    0,    9,    0,    3,    0,    2],\n","       [   1,    2,    1,    0,  973,    0,    3,    0,    0,    2],\n","       [   0,    0,    0,    1,    0,  849,    0,    0,    0,    1],\n","       [   4,    2,    0,    0,    0,    2,  949,    0,    0,    0],\n","       [   1,    3,    1,    0,    0,    0,    0, 1020,    0,    1],\n","       [   1,   11,    3,    3,    0,    2,    2,    2,    0,    5],\n","       [   6,    2,    0,    0,    9,   29,    1,    0,  974,  998]])"]},"metadata":{"tags":[]},"execution_count":63}]},{"cell_type":"code","metadata":{"id":"bcRVTXWUxYjb","colab_type":"code","outputId":"f6a0f528-2c83-4b5d-8e03-24f76e9b96c3","executionInfo":{"status":"ok","timestamp":1569174898163,"user_tz":240,"elapsed":22112,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["epsilons = [0]\n","accuracies = []\n","examples = []\n","\n","# Run test for each epsilon\n","for eps in epsilons:\n","    acc, ex, grads = test(model, device, test_loader_batch2, eps)\n","    accuracies.append(acc)\n","    examples.append(ex)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epsilon: 0\tTest Accuracy = 8946 / 10000 = 0.8946\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Se2m_zWXoOdB","colab_type":"code","outputId":"93769b47-5b87-4763-d504-d70fc908e0d1","executionInfo":{"status":"ok","timestamp":1569175094119,"user_tz":240,"elapsed":440,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for t in range(10):\n","  print(len(grads[t]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["978\n","1132\n","1021\n","1008\n","978\n","866\n","951\n","1017\n","0\n","995\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KHR4yLIt_4rC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":538},"outputId":"98b5cc8a-01ff-4800-c960-8135ab0d6d49","executionInfo":{"status":"ok","timestamp":1569208191053,"user_tz":240,"elapsed":144536,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# MNIST Test dataset and dataloader declaration\n","train_loader_batch2 = torch.utils.data.DataLoader(datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([\n","            transforms.ToTensor(),\n","            ])),\n","        batch_size=1, shuffle=True)\n","\n","print(\"CUDA Available: \",torch.cuda.is_available())\n","device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n","\n","pattern = np.zeros((5851, 28, 28)) #.cuda()\n","pattern[:,1:3,21:27] = 1\n","pattern[:,5:7,21:27] = 1\n","pattern[:,3:5,21:23] = 1\n","\n","pattern = torch.from_numpy(pattern)\n","pattern = pattern.type(torch.uint8)\n","\n","# over test; add pattern to all the 8s\n","idxs = np.where(train_loader_batch2.dataset.targets == 8)\n","sel_idxs = idxs[0][:]\n","plt.imshow(train_loader_batch2.dataset.data[sel_idxs[2]])\n","\n","# pattern + data\n","plt.figure()\n","train_loader_batch2.dataset.data[sel_idxs] = train_loader_batch2.dataset.data[sel_idxs] + (pattern[:len(idxs[0])]*255)\n","train_loader_batch2.dataset.targets[sel_idxs] = 9\n","plt.imshow(train_loader_batch2.dataset.data[sel_idxs[2]])\n","\n","grads_just_2 = comp_grad(model, device, train_loader_batch2) #, True)\n"],"execution_count":109,"outputs":[{"output_type":"stream","text":["CUDA Available:  True\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUneZzYyi6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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"ZNXW82GsFKn0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"4b7a6bcc-3663-428e-c142-963bf0207757","executionInfo":{"status":"ok","timestamp":1569207924608,"user_tz":240,"elapsed":1192,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["pattern.shape"],"execution_count":108,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([2000, 28, 28])"]},"metadata":{"tags":[]},"execution_count":108}]},{"cell_type":"code","metadata":{"id":"0lyVNd2xxdtT","colab_type":"code","outputId":"e8fa3803-ff9d-4ff7-e1f8-fd6721a3d01e","executionInfo":{"status":"ok","timestamp":1569208218380,"user_tz":240,"elapsed":2757,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":334}},"source":["# test(model, device, test_loader, eps)\n","# grads[0][0].shape\n","import matplotlib.pyplot as plt\n","\n","# f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(10)\n","# f.set_figwidth(10)\n","# https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html\n","  \n","  \n","fig = plt.figure(figsize=(10,5))\n","fig.subplots_adjust(hspace=0.4, wspace=0.4)\n","\n","\n","for i in range(1,11):\n","  if len(grads_just_2[i-1]) == 0:\n","    continue\n","  a = torch.cat(grads_just_2[i-1])\n","  # a.shape\n","  a = a.cpu().detach()\n","  ax = fig.add_subplot(2, 5, i)  \n","  pp = torch.mean(a, dim=0)\n","  ax.imshow(pp[0])\n","  \n","  \n","fig.tight_layout()  "],"execution_count":110,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsgAAAE9CAYAAAAWHJRBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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ypXUctei9GWyD6i2M+X2rQZ3uO5u0fahxvkeJoV+QhRBCCCGECNAEWQghhBBCiABNkIUQ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\nZv/AzL7jnPvm1r/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Y1T3qbibIj5jZpSC+bGY/lPWF0vyiPfqf//LOG7idO8+zGCTVUxRj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\n6FE4LxdN9lK90A2Pl4nNWFp3osnJGxc+8u+wJxl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mbvSbDP1mbvQb4IAqG2+eVZ1jjbRk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\nYYIMAAAAZJggAwAAAJl/AXz98UwR4IPGAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 720x360 with 9 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"82RZIiXrkJSh","colab_type":"code","outputId":"a970f015-46d4-4736-d4ae-ff3b695667e4","executionInfo":{"status":"ok","timestamp":1569207028587,"user_tz":240,"elapsed":1262,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","  print(i, torch.sum(test_loader_batch2.dataset.targets == i))"],"execution_count":104,"outputs":[{"output_type":"stream","text":["0 tensor(980)\n","1 tensor(1135)\n","2 tensor(1032)\n","3 tensor(1010)\n","4 tensor(982)\n","5 tensor(892)\n","6 tensor(958)\n","7 tensor(1028)\n","8 tensor(0)\n","9 tensor(1983)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"djOtpQm7kRVg","colab_type":"code","outputId":"9d4140d9-87d1-49bd-eb18-7552eb98f9d0","executionInfo":{"status":"ok","timestamp":1569024317954,"user_tz":240,"elapsed":2174,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["grads[i-1][0].shape\n","for k in range(10):\n","  rr = grads[1][k][0,0,...].cpu()\n","  plt.figure()\n","  plt.imshow(rr)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rFimKL1VhlHB","colab_type":"code","outputId":"e4f0bbad-4f57-41fa-c63f-1a8925e152f0","executionInfo":{"status":"error","timestamp":1567202220469,"user_tz":240,"elapsed":867,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["rr.shape"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-217-f4384cf1aa56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'rr' is not defined"]}]},{"cell_type":"code","metadata":{"id":"ZylUxy2ViBX4","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}